{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 前言\n",
    "\n",
    "python是当前数据分析最流行的工具之一，在做数据分析的时候我们经常会用到一些统计学的基础知识和概念，如概率，分布，直方图，箱体图，分位数等等。不可否认的是R语言及其工具是统计分析最专业的武器，正因为它太过专业(使用者需要具备统计学的专业知识)，使得它的受众局限于特定领域的专业人士,而python是后起之秀，它瑞士军刀般的灵活性正被越来越多的IT从业人员所使用，同时python也包含了很多第三方的数据分析工具库，今天我们就来使用python对真实房屋销售数据进行分析,同时还会对其中涉及到的一些统计学的基础概念进行讲解，希望我分享的东西对那些想成为数据科学家和机器学习工程师的朋友们带来一定帮助, 好了,废话少说，让我们撸起袖子，干起来吧！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据\n",
    "\n",
    "我们的数据来自于kaggle，它是关于房屋的销售数据，你可以点击这里进行下载。下面是数据字段的含义在下载页面中有介绍，我就不一一说明了。\n",
    "\n",
    "下载地址:https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "import plotly.figure_factory as ff\n",
    "import cufflinks\n",
    "cufflinks.go_offline()\n",
    "cufflinks.set_config_file(world_readable=True, theme='pearl')\n",
    "import plotly.graph_objs as go\n",
    "import plotly.plotly as py\n",
    "import plotly\n",
    "plotly.tools.set_credentials_file(username='XXX', api_key='XXX') #需要注册自己的账号\n",
    "from plotly import tools\n",
    "init_notebook_mode(connected=True)\n",
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这里我们用到了plotly库，plotly是开挂的作图神器，可以供js, python, R, DB等使用，我们用它来进行一些交互式的可视化操作如画直方图、箱线图等，但是使用plotly需要在它网站上注册一个账号,否则无法使用。朋友们可以在这里点击这里进行注册。\n",
    "\n",
    "注册地址:https://plot.ly/settings/api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "1         Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
       "2         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "3         Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
       "4         Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
       "\n",
       "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0       Norm     1Fam     2Story            7            5       2003   \n",
       "1       Norm     1Fam     1Story            6            8       1976   \n",
       "2       Norm     1Fam     2Story            7            5       2001   \n",
       "3       Norm     1Fam     2Story            7            5       1915   \n",
       "4       Norm     1Fam     2Story            8            5       2000   \n",
       "\n",
       "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "1          1976     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "2          2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "3          1970     Gable  CompShg     Wd Sdng     Wd Shng       None   \n",
       "4          2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "\n",
       "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
       "0       196.0        Gd        TA      PConc       Gd       TA           No   \n",
       "1         0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
       "2       162.0        Gd        TA      PConc       Gd       TA           Mn   \n",
       "3         0.0        TA        TA     BrkTil       TA       Gd           No   \n",
       "4       350.0        Gd        TA      PConc       Gd       TA           Av   \n",
       "\n",
       "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0          GLQ         706          Unf           0        150          856   \n",
       "1          ALQ         978          Unf           0        284         1262   \n",
       "2          GLQ         486          Unf           0        434          920   \n",
       "3          ALQ         216          Unf           0        540          756   \n",
       "4          GLQ         655          Unf           0        490         1145   \n",
       "\n",
       "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "0    GasA        Ex          Y      SBrkr       856       854             0   \n",
       "1    GasA        Ex          Y      SBrkr      1262         0             0   \n",
       "2    GasA        Ex          Y      SBrkr       920       866             0   \n",
       "3    GasA        Gd          Y      SBrkr       961       756             0   \n",
       "4    GasA        Ex          Y      SBrkr      1145      1053             0   \n",
       "\n",
       "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "0       1710             1             0         2         1             3   \n",
       "1       1262             0             1         2         0             3   \n",
       "2       1786             1             0         2         1             3   \n",
       "3       1717             1             0         1         0             3   \n",
       "4       2198             1             0         2         1             4   \n",
       "\n",
       "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
       "0             1          Gd             8        Typ           0         NaN   \n",
       "1             1          TA             6        Typ           1          TA   \n",
       "2             1          Gd             6        Typ           1          TA   \n",
       "3             1          Gd             7        Typ           1          Gd   \n",
       "4             1          Gd             9        Typ           1          TA   \n",
       "\n",
       "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
       "0     Attchd       2003.0          RFn           2         548         TA   \n",
       "1     Attchd       1976.0          RFn           2         460         TA   \n",
       "2     Attchd       2001.0          RFn           2         608         TA   \n",
       "3     Detchd       1998.0          Unf           3         642         TA   \n",
       "4     Attchd       2000.0          RFn           3         836         TA   \n",
       "\n",
       "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "0         TA          Y           0           61              0          0   \n",
       "1         TA          Y         298            0              0          0   \n",
       "2         TA          Y           0           42              0          0   \n",
       "3         TA          Y           0           35            272          0   \n",
       "4         TA          Y         192           84              0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
       "0            0         0    NaN   NaN         NaN        0       2    2008   \n",
       "1            0         0    NaN   NaN         NaN        0       5    2007   \n",
       "2            0         0    NaN   NaN         NaN        0       9    2008   \n",
       "3            0         0    NaN   NaN         NaN        0       2    2006   \n",
       "4            0         0    NaN   NaN         NaN        0      12    2008   \n",
       "\n",
       "  SaleType SaleCondition  SalePrice  \n",
       "0       WD        Normal     208500  \n",
       "1       WD        Normal     181500  \n",
       "2       WD        Normal     223500  \n",
       "3       WD       Abnorml     140000  \n",
       "4       WD        Normal     250000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/house_train.csv')\n",
    "df.drop('Id', axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 单变量分析\n",
    "\n",
    "在统计学中,我们把变量一般分为两类:分类型变量和数值型变量。数值型变量又可分为连续型和离散型，分类型变量又可分为普通型分类变量和有序型分类变量:\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "我们使用describe()命令可以查看数据中说有数值型变量的分布情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>1201.0</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>21.0</td>\n",
       "      <td>59.00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>7553.50</td>\n",
       "      <td>9478.5</td>\n",
       "      <td>11601.50</td>\n",
       "      <td>215245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1954.00</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1967.00</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>2004.00</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>1452.0</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166.00</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>383.5</td>\n",
       "      <td>712.25</td>\n",
       "      <td>5644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>46.549315</td>\n",
       "      <td>161.319273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1474.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>567.240411</td>\n",
       "      <td>441.866955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>223.00</td>\n",
       "      <td>477.5</td>\n",
       "      <td>808.00</td>\n",
       "      <td>2336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>1057.429452</td>\n",
       "      <td>438.705324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>795.75</td>\n",
       "      <td>991.5</td>\n",
       "      <td>1298.25</td>\n",
       "      <td>6110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>1162.626712</td>\n",
       "      <td>386.587738</td>\n",
       "      <td>334.0</td>\n",
       "      <td>882.00</td>\n",
       "      <td>1087.0</td>\n",
       "      <td>1391.25</td>\n",
       "      <td>4692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>346.992466</td>\n",
       "      <td>436.528436</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>728.00</td>\n",
       "      <td>2065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>5.844521</td>\n",
       "      <td>48.623081</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>1515.463699</td>\n",
       "      <td>525.480383</td>\n",
       "      <td>334.0</td>\n",
       "      <td>1129.50</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>1776.75</td>\n",
       "      <td>5642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>0.425342</td>\n",
       "      <td>0.518911</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>0.057534</td>\n",
       "      <td>0.238753</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullBath</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>1.565068</td>\n",
       "      <td>0.550916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>0.382877</td>\n",
       "      <td>0.502885</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>2.866438</td>\n",
       "      <td>0.815778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>1.046575</td>\n",
       "      <td>0.220338</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>6.517808</td>\n",
       "      <td>1.625393</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>0.613014</td>\n",
       "      <td>0.644666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>1379.0</td>\n",
       "      <td>1978.506164</td>\n",
       "      <td>24.689725</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1961.00</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>2002.00</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>1.767123</td>\n",
       "      <td>0.747315</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>472.980137</td>\n",
       "      <td>213.804841</td>\n",
       "      <td>0.0</td>\n",
       "      <td>334.50</td>\n",
       "      <td>480.0</td>\n",
       "      <td>576.00</td>\n",
       "      <td>1418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.00</td>\n",
       "      <td>857.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>547.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2007.00</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2009.00</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>180921.195890</td>\n",
       "      <td>79442.502883</td>\n",
       "      <td>34900.0</td>\n",
       "      <td>129975.00</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>214000.00</td>\n",
       "      <td>755000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count           mean           std      min        25%  \\\n",
       "MSSubClass     1460.0      56.897260     42.300571     20.0      20.00   \n",
       "LotFrontage    1201.0      70.049958     24.284752     21.0      59.00   \n",
       "LotArea        1460.0   10516.828082   9981.264932   1300.0    7553.50   \n",
       "OverallQual    1460.0       6.099315      1.382997      1.0       5.00   \n",
       "OverallCond    1460.0       5.575342      1.112799      1.0       5.00   \n",
       "YearBuilt      1460.0    1971.267808     30.202904   1872.0    1954.00   \n",
       "YearRemodAdd   1460.0    1984.865753     20.645407   1950.0    1967.00   \n",
       "MasVnrArea     1452.0     103.685262    181.066207      0.0       0.00   \n",
       "BsmtFinSF1     1460.0     443.639726    456.098091      0.0       0.00   \n",
       "BsmtFinSF2     1460.0      46.549315    161.319273      0.0       0.00   \n",
       "BsmtUnfSF      1460.0     567.240411    441.866955      0.0     223.00   \n",
       "TotalBsmtSF    1460.0    1057.429452    438.705324      0.0     795.75   \n",
       "1stFlrSF       1460.0    1162.626712    386.587738    334.0     882.00   \n",
       "2ndFlrSF       1460.0     346.992466    436.528436      0.0       0.00   \n",
       "LowQualFinSF   1460.0       5.844521     48.623081      0.0       0.00   \n",
       "GrLivArea      1460.0    1515.463699    525.480383    334.0    1129.50   \n",
       "BsmtFullBath   1460.0       0.425342      0.518911      0.0       0.00   \n",
       "BsmtHalfBath   1460.0       0.057534      0.238753      0.0       0.00   \n",
       "FullBath       1460.0       1.565068      0.550916      0.0       1.00   \n",
       "HalfBath       1460.0       0.382877      0.502885      0.0       0.00   \n",
       "BedroomAbvGr   1460.0       2.866438      0.815778      0.0       2.00   \n",
       "KitchenAbvGr   1460.0       1.046575      0.220338      0.0       1.00   \n",
       "TotRmsAbvGrd   1460.0       6.517808      1.625393      2.0       5.00   \n",
       "Fireplaces     1460.0       0.613014      0.644666      0.0       0.00   \n",
       "GarageYrBlt    1379.0    1978.506164     24.689725   1900.0    1961.00   \n",
       "GarageCars     1460.0       1.767123      0.747315      0.0       1.00   \n",
       "GarageArea     1460.0     472.980137    213.804841      0.0     334.50   \n",
       "WoodDeckSF     1460.0      94.244521    125.338794      0.0       0.00   \n",
       "OpenPorchSF    1460.0      46.660274     66.256028      0.0       0.00   \n",
       "EnclosedPorch  1460.0      21.954110     61.119149      0.0       0.00   \n",
       "3SsnPorch      1460.0       3.409589     29.317331      0.0       0.00   \n",
       "ScreenPorch    1460.0      15.060959     55.757415      0.0       0.00   \n",
       "PoolArea       1460.0       2.758904     40.177307      0.0       0.00   \n",
       "MiscVal        1460.0      43.489041    496.123024      0.0       0.00   \n",
       "MoSold         1460.0       6.321918      2.703626      1.0       5.00   \n",
       "YrSold         1460.0    2007.815753      1.328095   2006.0    2007.00   \n",
       "SalePrice      1460.0  180921.195890  79442.502883  34900.0  129975.00   \n",
       "\n",
       "                    50%        75%       max  \n",
       "MSSubClass         50.0      70.00     190.0  \n",
       "LotFrontage        69.0      80.00     313.0  \n",
       "LotArea          9478.5   11601.50  215245.0  \n",
       "OverallQual         6.0       7.00      10.0  \n",
       "OverallCond         5.0       6.00       9.0  \n",
       "YearBuilt        1973.0    2000.00    2010.0  \n",
       "YearRemodAdd     1994.0    2004.00    2010.0  \n",
       "MasVnrArea          0.0     166.00    1600.0  \n",
       "BsmtFinSF1        383.5     712.25    5644.0  \n",
       "BsmtFinSF2          0.0       0.00    1474.0  \n",
       "BsmtUnfSF         477.5     808.00    2336.0  \n",
       "TotalBsmtSF       991.5    1298.25    6110.0  \n",
       "1stFlrSF         1087.0    1391.25    4692.0  \n",
       "2ndFlrSF            0.0     728.00    2065.0  \n",
       "LowQualFinSF        0.0       0.00     572.0  \n",
       "GrLivArea        1464.0    1776.75    5642.0  \n",
       "BsmtFullBath        0.0       1.00       3.0  \n",
       "BsmtHalfBath        0.0       0.00       2.0  \n",
       "FullBath            2.0       2.00       3.0  \n",
       "HalfBath            0.0       1.00       2.0  \n",
       "BedroomAbvGr        3.0       3.00       8.0  \n",
       "KitchenAbvGr        1.0       1.00       3.0  \n",
       "TotRmsAbvGrd        6.0       7.00      14.0  \n",
       "Fireplaces          1.0       1.00       3.0  \n",
       "GarageYrBlt      1980.0    2002.00    2010.0  \n",
       "GarageCars          2.0       2.00       4.0  \n",
       "GarageArea        480.0     576.00    1418.0  \n",
       "WoodDeckSF          0.0     168.00     857.0  \n",
       "OpenPorchSF        25.0      68.00     547.0  \n",
       "EnclosedPorch       0.0       0.00     552.0  \n",
       "3SsnPorch           0.0       0.00     508.0  \n",
       "ScreenPorch         0.0       0.00     480.0  \n",
       "PoolArea            0.0       0.00     738.0  \n",
       "MiscVal             0.0       0.00   15500.0  \n",
       "MoSold              6.0       8.00      12.0  \n",
       "YrSold           2008.0    2009.00    2010.0  \n",
       "SalePrice      163000.0  214000.00  755000.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同样我们可以使用describe(include=['0'])命令来查看数据中的所有分类型变量的分布情况:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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    }
   ],
   "source": [
    "table_cat = ff.create_table(df.describe(include=['O']).T, index=True, index_title='Categorical columns')\n",
    "iplot(table_cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 直方图\n",
    "\n",
    "对于数值型变量，我们一般会用直方图来展示其数据的分布，下面我们画一下房屋销售价格(SalePrice)的直方图，其中参数bins表示X轴被划分若干区间的数量,y轴表示对应的价格区间在数据中出现的数量(频率)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SalePrice'].iplot(\n",
    "    kind='hist',\n",
    "    bins=50,\n",
    "    xTitle='price',\n",
    "    linecolor='black',\n",
    "    yTitle='count',\n",
    "    title='销售价格直方图')"
   ]
  },
  {
   "attachments": {
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rmbYkzibMBZXqxdA6ljJqnlisREiJa/iUlJRyUSV50p4IHnlGIq4kFmElFilVUIlHREmrsaRV4aQKKXUsamx+XfKk3YZsQWwkr8wFdzOs8xAMH5uPrB1J0KfuwpYV9tAUZ+JkeiHg1wxdbrkLY7tEQLNrF2K278PJ07mATovcjAxktu2NUVPuxtV+J7ElsQgFwFmRpEdZbgL2bNyIRIsemmA05OHYP39iN4DAnreief42bFsZi39X5CFyZHeEuzvj3AeKDwbceNuZFlIykJNXAl2VIqkIpw5uxIpfNDhcqTUlG7HbjyENgAdKkJ24D9v358O31/Vn1l1Z9FeHwvS9WPjS4/hMOxbJT/RDAACn/v3RT8ol/Y35e/bCd9AgtFkbj/XffIe4ndlw6XQH7KN/x6/xZ6bYZW+Zi/cX7UWa92A8+c4rmHbLVWjqZbkfXc7erdibkoiSlkPRIcQeBSc24liKWWcKTuPAiVRkwwTPpKPYvbG4XNy1GvYsxnRqjYI925CXkw0tKJLMyDFJArUmcO7foFpXZQUSIAESIIGGQECmxakipDbCSqbuqYJJYvOgiinrPFVYiWv3hIQERViJoFKFlQgeEVMiftQg66hUMaUKKonVtAgvKauOQRVP5udqWi1Hi9WFvnm5OLFtH6ITgNDenREV2Rd3Px2BoPfewh9J4Rj42NO4zXkD3n37DUQfCke3Ln3Ru7kzdL1vxuNNU/HXpy9j07wCdB07HV9N7YGyuANYF+2GATd2QpjaNUdP+Hcagzd+sPwhxGTUIffwj3h653LsNnXH3a99jveGh8E58UvctP4PtLthCLq16o1h/aMQ6OZUBycOJUiP24cta7IRbalB1J4BKEZmfAJ0IaFo0z4ERTvm4fOFibj60/NFkrE0BylHVmLVtjKUNj2IDauKYD2h0M7eHqNnzQJmzcL6Q964+ckJ2Lo1BpvWRp+9p3isO4gch0j07+KC9LRcFJfoAHORpEnG5o3bkXhKg4539Mcdj92DgS3tkRGzD4cz7REa2QHtC//F6zNn4sAJI3re9CRmvn87mmUcx7FDu3HKEIE2nXui003DzcbJJAmQQF0JUCTVlRzrkQAJkEAjJiDCSgSHiKqaCisZroghcwElXv7MhZSaNs+XqYBSRyxW5oJK0iLUVIuVKoQkFuGkCik19vb2rlBYqWJM6qkiqqKYwsr8hc1DwqbF+OTTI/C6ZSjGTRqH/qGZOJlYhNTkdMTuXIUlRXtxOCEHbho7HFsxFy/sOwpNp9G4Z2wITsdkACYDck7vxb/zD+DAmvl4c1kqWg1+BO++Pxl3RFXmadIIQ9kpbJj1LubHO6Dlg2/gueFhcHKwAzw84G23CXMe347Ihz5B5x6t4F8nkRSEXrc9jZeqXJOUheg1f2LjthK0uXkAylbPxPd6PTprSgHzFU+6YmQf2YwF3/yGXR7+6DLqVXw9eyi8c45jy7ojyDFHGhcHjBqFUWfzbrnlzEIlvw7Xo5vz73huey78hjyGd2eMRc9Qb1g7uis9uRp/bz6JE+me6JpfgmJNCUpgxNG/5mDmH6kI7zcatwYlItXkhZbNAU3MdvyzxASXI3/jz4VfY4t+MO594QPMnNgNlS7FMu8v0yRAAlUSoEiqEg8vkgAJkAAJmBNQhUxdhZWIJ1VAVRWLxUrWqqWlpZ0nrPR6vSKspC/q9D2JRVipQqqiWMqogso6VgWWdf6VJ6zEeYA45GiJGyZNwsnEF/D+vFfwapET/jPQCfvT4nAgyQUlv8dh5alYxMbEI6ckDtvTIhAZ1QmtPePx13drcfzwEaAI2Dn/XUze3hl9WkXg1iERgG8Wdh3OrFQkGfWlSNr7Feb8FA/7Vtfg+cdHIMz+TI8QHIKm9vZwQCTuuH8oWvm4m025M38Lq0uXIL+wAEXFOsDXWoqodQPQfvCDaD8YKI1fix9PxiA6wwPH9h1BXr8e50SGtggFqcdxFF1x2+gQ9Lq1FwINGmTFLMaLd7yBfU7N0eumzmemF6pNK3EGDq/chQStHi2fX4WFfdLgct39mP7SbehagUAC8nB03WYcTExFAQqxd902HBh9A67ucMYxA2LXYMWJFMS1bYNrht+C53s7ImHtWqz76SjyU+Jx/BRQ6pyH9BPJyAVFksWj4AkJ1JEARVIdwbEaCZAACZBAzQjUVljJGit1KqC1qFLPK4rFM6BMAzx16pQirFRLlcQSZG2VCCU1iKiSqYBiqRJRVVGwFlbmUwkrSktew15jZQCUCWz2gG93DBrcFcu37cCG7NPYsjEJGYZI3PvydEwZ6oKNX8zE7B918ChxgrNfCwy470U8OyICGevm4o13MuHi2Aw+3m6w7/0gpj81Cr1blPvDO//FMOpQlnYUf2+Pxo4vZmFtYBcMnDIbj191ftHzcnLjsOWkPSLbNkWQp0sV0++8EBDqD2+/XBzZ+S9W/OaNohp42suJXY812w+gMCEYe//8E7vu64Eh6lA8ghF584v4/WazXukLz544wcVjJGb8/jGGml0+k1yNZwPG4MtsL3h4mxA85AV8efs5t+rWxcvSN2HZsqNIzSiDszMQNqAnurZtjkCccwrRdtB9eOWlabgp8mztp56CiKuDv83G21Nfx7qA5uh0fU+0tG6c5yRAAnUiQJFUJ2ysRAIkQAIkcLEI1HUqoCqsxG26KqKqS4uwys3NVUSZ1FeFlaQliOARMWUexKW6CCsfH59yYWWelrKqgJK4qrR6/dIJK9lx55xr9xbdhqH/jXlo4e2D0u3HoXHugrbh3ijY/gdW70qCy+AnMb2NCVu/fhu/ffshXLNvgFfMHpxsOx4vPXoXWsR9hQlz3sHjpTr89+EhaN+jebkjAYv3o6wQOetm4dZ75sPeOQSdJr6OeU/0sChS4UluPLYtfBcPzAfGT56Ah0d0R3ilQqkl+o6+BSML9fh78x7Mf+03fOnWDK1atkBAuQMHccKQiJPHM6Bv0hJtOoTCS24cOARDhgBBUQYUiTcHVSRV2KnaZA7Esw9cDT/vygUSUIDoJb9jn6ElIntmwhATV+EN8lPisG/zajidML9cjBMHTiCtxDyPaRIggfogQJFUHxTZBgmQQJ0JpKenK57VoqKilA9R7s1TZ5Q2X1EER5MmTZRQUxjmwkoVVBJXlRZRJe+tKqTMY4PBoEwFVK1U6h5V4rhCtVSJoFKDmicWLVUwSay6a1fTIqIkT2JpU6xzdTtk+tk5kYSWQzHl7aEo3jkXz637C7owD5hSN2Duz38g2nEAJj0wCbf7x8K34Ahi/zqKjUt/g96tNca++AQeuLE1cuNK8Oz+tXh17nTcs30fps59CANadkKU+S6uJj20hck4HJ0OQDwldsSESd2Rtn49Yi0GcRCndQYYUIiE/VuxWecHjyPf4eGvj8DPsRD/e1uL4OAZGHdtK/hVMovOt/td+E/3uzBhw2y8+PoH2OY6EPc99QLuvKY5Qr2lUh72L/4Qb734E061vwtTZz2MIe1C4GnRj5qeGGHUJ+Hg+vU43z/EASTp9BC7XXVHYdJKLPk3BZ1GTUXXIi3mJsXB3KGdWj/lwFoszIvDGotNk/QoykzCqTwAzdSSjEmABOqDAEVSfVBkGyRAArUiINOpZM1JfHw8Fi9ejNWrV+OHH35AmzZtlI1Sa9UYC5PABRBQhZWfnx+MRiPk3awuFucVYqkSsZSXl6fElaXlenJysuK4QsSUiCgJcg81SPdlLZQ67U+1SqmxCCwJIqQkREZG4pZbblHEWO2HbiaQyiuX4Mi+PUiI08M96BiWL1iLo3E+6DcuEu76VCTYN0f/Ebcj8WQeftjugiEPP4pnbmqGoqxEpKQXI6DnGAzbvQ2J7gew5MM5yH3kHUwbpOyUBIhAyonDjmXf46PFMYoY0ZsM0KdtwldvzMHh8j5IIg8nS0qhQzLWfv0Ojnk5w9EOCB35Er690w5LP/wJWalFKNSgUpFk0RyA6LVLMC+gOZpHTMbojubKKgH7Ni7F/G86ofMHt0LdH9e6foXndg5w8opA12t7wRmp+P2ll7BUk4JD+xJQiGC06xuJJvZ2sOvcHT3RFkEujufvh1TecCF2/m8hDkbdjGeGd4Pfv+UXzku0Hz4Jr5pPt1NKmE23O68GM0iABC6EAEXShdBjXRIggVoTkEX3SUlJ2Lp1Kz766CPlF/mrr74a8pFKK1KtcTaqCiJAzIO1GKlIoFxIntp+dW3IdREw8m6aT7czT8v1mp6LCJK1TAEBAYrFqGnTpkpd8w1+RWipbarCSWJxViFeAOVQ/x6kf3Ko4xBL0k033VRHkaQ0ZfkfzWHs35WIuIwMaHftQ2jUINw3PgAp+z/GTV/5YPyYIehbtgc/rS9A+MDbMag9ELdzDY79uxhzpn+PxC6DMem9P/HdjX6WFhWDFqXpR/DvH9/inffTcPf7E5B9x0wcsneCc+R4fLF5vGU/sBrPBY7BF1lt8dB//8SUvsHwMdM1075X/cZZVavqNOQaDLhhMAZ09D6vVJNOkeg+tA9a64qRk34acSkFgIsnfEKaIyrY2sm36L0yFKbF4VhKMYCOmPjRR2faNOlhSFyER+/8BIfQBw+/NxXXmgujhGgcSJANmcLQtnUIfFwczWx5p5CZMxSTxg1Bt9Z+SKxCJBVlJSH2wE4EnHk9zo6nCHHHU5B9bunSeeNkBgmQQN0IUCTVjRtrkQAJ1JKAfLDKr+rR0dH48ssvsXLlSjRv3hwzZszAPffccwHTh2rZERsoLh/TajAXCupHtnrN+ry+ylbWjogAESKqODCPrdPm5+biRE2rsVpO2lXblvtIvppnHcs1875IWg4RJWoQr3ZqWuKqzqu6JtPj1Gl0Yi2S6XUinszzJC3XrPNkep36jKSP6rjEklR/a5g0yNm+HZtjTyCt9zi899oLeKJ/U+D4H5i5/zcgdiv++roQduNvxJ33a7B54TeYvu0bADqU5GUiBQ7QZRXh8L79yLtxoJmXNwN0eXHY9tUruP27DAwY9RSubXoS39Xz319J1gkkZ+SjyEok5B1PQk6hDtAWIDMxBgf3OZ1dZlSImJNpyCsD9MV5SI07jH2mFKxe+j3e/j0O/qFNEXHLi1g0bSSCytcxnem0riAVu768D4PfOgBH76Zo28ofZyY9mmAqzUS8CFlsxtwnEzFfLElKNS2yT8QipUAHfdRzWPrni7ixdYCZk/GOuOuzjuVULDfdLc9WEie3/ob/xm/BPIuZlgZo8jKQngk4NbUszzMSIIELI0CRdGH8WJsESKAaAvKRJ7+aJyYmYtmyZZgzZ47ygTd+/HhMnToV8it7Yz0qExvm+epHrnWeem4tKKrLr+66tKd+TIuQUMVEZbEqMtTr1udqvhqrQkQVHuZiRE2rsXlZyZO+mQsP87S10FCvVZZvfV0VGKrgENGhrvORtCpCKorVPLVuTWPretbn0k79iZl6+isx6QA7B1EIyEjJQE7BASz9fBG2bjqBro+0RNsI67/HKNxw7wuYPmsMOgCYOk3tRwr2LP4c08d+gaS2/XDP/eYCScpokJtzCCv+KcUNz8/BX49FIXvXbLVyvcUJ/36C975fi71J5k3qUZydhozMPLh52WPzgvdxYIl63YDS/CxkZgLFuTuw5P3J2ODpCIRfhQdemYzRdn9j5voVWBU/Eved2eZIrXgudvZGkwGvYPmyCWc8yRl10MV8hus7PIdtGI2Pts3BCDens+7LT+Lb0ddi6qpU6Au1KDYaoT/XUq1SnUc+xel2tSLGwiRwYQQoki6MH2uTAAlUQUA+kjMzM7Fr1y58+OGHOHbsGLp3745p06Zh4MCBVdQ8d0lEgRyViQlVNFyOWD78RTyYB8kzzzdPq0LDOs86X21DFRlyrqbVuKo8YaGKiPqM5YPfWgjIubkYUQWGWk5iNcg18+vmZarLr+i6miexjJNHDQgYsgB7byBvHxa9+wkWrN2LhLRMZMMJYQ6OcLC3bqMMhfJdUDgAACAASURBVDkpSIiJsVpXk4HElGzIxLOKD08EtbkTs7beeeZyudvsikvXNbfDnZ/gh7O3KG/DkIS/P5iKmR//DvvRL+KV6c9gSLlTA9Vxw5vYFDwMT77+KV4aElxeFRiNAY+ZndZjcthrT2BgaFAdnUTUY0fYFAmQQI0IUCTVCBMLkcCVRUAVHOqo1F/r1fMLjaV9ca0sU+u+/fZbLFiwQPHINWHCBEiQ9RopKWf8N1UlbqQfqiiwFiJVnauiw7xMRXnmQkMVJtZ5Uk/tg3WZqgSI9L2q6+pHfUVlzD/+VYEhsbkYMRcY1umKzivKU+8j19RQVV6Ds4pc6Itqi/UdAwCROwH9MXlOf0w27MP3j7yI976JQ2CTIPie5/o6Hlt/exvRGz6xXG8EPcqKC5AD+0uyL4++KBNpOcVw9AmBv5crnM4Tc+rDNECTuAe798YjobgPRrbvhrblAkkt0zjj0sIcpJ06gRMWvwcUICk9D0W6xjkm9poEGjIBiqSG/HSq6Zv8vn7mA1M+xs5+kFVTh5dtl4C8KyJe5EO/sLAQsohc8mTdg3jOEpfF8hEsgulCD2n/559/xuTJk5WmgoOD0bFjRxw8eBCPPvpo+XoRVbhIn8zFiHm+9FEOczFRH+eqVaQi8SBiRBUN5nFFAsL8upquSbnKyki+KqCUgfM/JFCvBMw8IUi7GelILsiHhS8Ai/t1wLAJL2Hm2el25y6p0+3m4LRJ1nidu1J1ygSTsRR5qadx+jy/2ZkoNJpgghYFGSlITtKi4OxXSsqy5/HQuyvQ9MF5mDlxBK4Kd6lQKOmKT2D1j9/jz+1H4TfqRQwe2B3Nq+7QRbkqoi49rwxGUypyNAYYTc5wdJQ1b3W/XfTKLzFty89ws/hyM0KnKURBHuAZWve2WZMESOB8AhZ/audfZk5DJCAfjQajCaU6A4q0OugMRjg5OMDT2RGuzg5wsJMFxw2x5+zT5SIg74yIIpn6lpaWBo1GowgktT8iGMS7XEhICHx9fRXhpF6rS1xUVIRDhw4p4ktcfWdlZWHfvn3w9/dX3BzLHi8iKCoTI6rYUGMpX5moqKyMeg+5LnWtz2XMFCN1ebqsc0URyExDYmE+Mus8qDwUl8QhWTZgjahJI3poS3bg/dt6478OFZR39oBPSAZ+fvIm/HzeZS+kfzIOr7v9ifcnDEanIPMfdIwoKyrA7nkz8NmSfxDd/jG8N+le3NblPNPYea3WLkOceDjDy9vdavqh2ooeeoMJyb9Oxog3tiInKxPZRVo4ew1Eh5YecD1PGKr1qo+7j/1PlWuS/tWWoig3A2lpJjg4u8LDyxfuVpq4+ruwBAmQgEqAIkkl0Uhio8mEfI0WidlFSM4rRon23BJQD2dHhPt5oJm/J3xcXSiUGskzvdjdFIEkoiguLk7xLifWJOtDFvqLkJG9XyIiIhAaGnpBQkmcMbz33nvKuqPZs2fj8OHDigCbMmUKbrvtNmXqnQgXHiRAApeZgKOL8jHt76+Fp7szHM77gU0PraYQ+dnZVtamXOQXaVD7WV5OcPUdglk7/8TEFrUf++7/jsBzJ44hM7cnENQE+tJClJRqoTNkYN2HL2L2gnU40nEyZr04GROvq1q1yf9P5QfHmh529g5wcgtCy1adMOOt8ZXs3RqDkwkm3HLvQhy7Hzj1490YPmMdrp35LSb2bgq/KkWS7J0lnikBQ1kxCvPEHXwZCrR2cHByhr2hBAW52TjrIf5st/ORV6yDydEVRUdX4cN7V+NTZye06D4Ik95diKf6uZ51IFHTUbIcCZCASoAiSSXRCGL5hzOzsBTRablKbN3lYq0eMen5yCkpQ+ewJmji6Wq2F4N1aZ7bCgHxLHf8+HFFIKlT1yobu0xzO336tHI5LCzsgjxzyRS+MWPGYNiwYVi0aJGyJ9JTTz2luP+ePn06hgwZolh3xKLDgwRI4DIR6DAOHy8ah4+tb2/vBBc3D3h7x2LjT88owbqIcu7tjXAPdzhX+2dsBzsHF3g0CYBf/1EYXAeBJPfrOXkFNph15Mj8RzDt0xXYJvsQoRVuf3Uhvpw4HBVsi3S2lh3sHV3g7umNMr0BqYriCDFrsfKkk28E+k//C3HTrcrIdHd7Z3h4e8MbHhYsmt07H0futSpf6WkGsrJEJHkj65+P8Og/Z/dhuvph3H/rHcjfMxuPDKrAQ2BwXwy+/ilMufYfLIxvhxE3DUDQ1jlY8dmPuL7fRFxV6f14gQRIoCoCdqbqvpqqqs1rl5RAdlEpjqTmIr1AU+19g7xc0T0iAN5uVf5sVW07LND4CcTHxytT7GTdT00PmXInexjJFLz6OOSfmfz8fHz99dcQy5JsltmvXz+8/vrr6NmzpzIdjlPf6oM02yABEiABEiABEqgPAuYTeuujPbZxkQjIR2ZaQQlEKNXkKCjVISmvcuesNWmDZRo/AbEi5ebmKo4SajMacbwgob5+QxEBJMLrueeeU9YmybS7nTt3YsCAAbj77rsV1+Ay5Y8HCZAACZAACZAACTQEAhRJDeEp1KAPsg4pr0QLfQ3nT4tTh6yiUpTqzq1ZqsFtWOQKI5CTk1NrgSQIRLCISBKnC/V5iFgKDAzEm2++qaxTeuihh5QNZrt164Z58+YpHu7q835siwRIgARIgARIgATqQoAiqS7ULkOd3BItxDpUm0O83sk6JR62S6CuIkmIqXsDXQx64mY8PDwcn3/+uWJZuv/+++Hj40NvcxcDNtskARIgARIgARKoNQE6bqg1sstTQaPTK4tMa3v3s1vM1LYay18hBMTtd0Xe7GoyvPqaalfZvcSqJHs0tW/fHp999pmSphOHymgxnwRIgARIgARI4FISoCXpUtK+gHsFeLrCt5ZOGOzt7ODswEd8AdgbfdWAgABlP6K6DESsPfWxsWx195Z7yB5GFEjVkeJ1EiABEiABEiCBS0WAX9CXivQF3sfP3UXxVHfeFhaVtOtobw9vV2d4XcjOdZW0zezGQ0C804m1pi6HbPQqgQcJkAAJkAAJkAAJ2BoBiqRG8sSdHOwR4OFaY9Hj4eKIEB83bijbSJ7vxeqml5cXZL+i2lqERBx5e3vXWWBdrPGwXRIgARIgARIgARK4FAQoki4F5Xq6R6iPO5r7e8LNqepd+zycHdHC3wvB3m71dGc201gJyLqf4OBguLq61ngIqrtuEUk8SIAESIAESIAESMAWCVAkNaKn7uRoj5YBXmgX4gtvV6fzem5nB/i4OSMqxBctAzwhU+54kIBMuZONYd3cqhfNIpCCgoIQFhamrBMiPRIgARIgARIgARKwRQJ2povtwsoWqV7kMYtr7xKtXtlYNqekDHqDEc6ODpB1S/6eroqlSabn8SABlYDse5SdnY3k5GRl/6OKPN45OzsrAikkJAQeHh50x63CY0wCJEACJEACJGBzBCiSGukjF9feBqNR2VzWBBPsYAdHezs4ONijps4dGunQ2e06EhChpNVqFZGUm5sLjUajuAcXxw4ytU4sTmJtcnJyokCqI2NWIwESIAESIAESuDIIUCRdGc+RoyCBGhMQsSQbxarWJJliJ+63RSxJmgcJkAAJkAAJkAAJ2DoBiiRbfwM4fhIgARIgARIgARIgARIgAQsCXLhigYMnJEACJEACJEACJEACJEACtk6AIsnW3wCOnwRIgARIgARIgARIgARIwIIARZIFDp6QAAmQAAmQAAmQAAmQAAnYOgGKJFt/Azh+EiABEiABEiABEiABEiABCwIUSRY4eEICJEACJEACJEACJEACJGDrBCiSbP0N4PhJgARIgARIgARIgARIgAQsCFAkWeDgCQmQAAmQAAmQAAmQAAmQgK0ToEiy9TeA4ycBEiABEiABEiABEiABErAgQJFkgYMnJEACJEACJEACJEACJEACtk6AIsnW3wCOnwRIgARIgARIgARIgARIwIIARZIFDp6QAAmQAAmQAAmQAAmQAAnYOgGKJFt/Azh+EiABEiABEiABEiABEiABCwIUSRY4eEICJEACJEACJEACJEACJGDrBCiSbP0N4PhJgARIgARIgARIgARIgAQsCFAkWeDgCQmQAAmQAAmQAAmQAAmQgK0ToEiy9TeA4ycBEiABEiABEiABEiABErAgQJFkgYMnJEACJEACJEACJEACJEACtk6AIsnW3wCOnwRIgARIgARIgARIgARIwIIARZIFDp6QAAmQAAmQAAmQAAmQAAnYOgGKJFt/Azh+EiABEiABEiABEiABEiABCwIUSRY4eEICJEACJEACJEACJEACJGDrBCiSbP0N4PhJgARIgARIgARIgARIgAQsCFAkWeDgCQmQAAmQAAmQAAmQAAmQgK0ToEiy9TeA4ycBEiABEiABEiABEiABErAgQJFkgYMnJEACJEACJEACJEACJEACtk6AIsnW3wCOnwRIgARIgARIgARIgARIwIIARZIFDp6QAAmQAAmQAAmQAAmQAAnYOgGKJFt/Azh+EiABEiABEiABEiABEiABCwIUSRY4eEICJEACJEACJEACJEACJGDrBCiSbP0N4PhJgARIgARIgARIgARIgAQsCFAkWeDgCQmQAAmQAAmQAAmQAAmQgK0ToEiy9TeA4ycBEiABEiABEiABEiABErAgQJFkgYMnJEACJEACJEACJEACJEACtk6AIsnW3wCOnwRIgARIgARIgARIgARIwIIARZIFDp6QAAmQAAmQAAmQAAmQAAnYOgGKJFt/Azh+EiABEiABEiABEiABEiABCwIUSRY4eEICJEACJEACJEACJEACJGDrBCiSbP0N4PhJgARIgARIgARIgARIgAQsCFAkWeDgCQmQAAmQAAmQAAmQAAmQgK0ToEiy9TeA4ycBEiABEiABEiABEiABErAgQJFkgYMnJEACJEACJEACJEACJEACtk6AIsnW3wCOnwRIgARIgARIgARIgARIwIIARZIFDp6QAAmQAAmQAAmQAAmQAAnYOgGKJFt/Azh+EiABEiABEiABEiABEiABCwIUSRY4eEICJEACJEACJEACJEACJGDrBCiSbP0N4PhJgARIgARIgARIgARIgAQsCFAkWeDgCQmQAAmQAAmQAAmQAAmQgK0ToEiy9TeA4ycBEiABEiABEiABEiABErAgQJFkgYMnJEACJEACJEACJEACJEACtk6AIsnW3wCOnwRIgARIgARIgARIgARIwIIARZIFDp6QAAmQAAmQAAmQAAmQAAnYOgGKJFt/Azh+EiABEiABEiABEiABEiABCwIUSRY4eEICJEACJEACJEACJEACJGDrBCiSbP0N4PhJgARIgARIgARIgARIgAQsCFAkWeDgCQmQAAmQAAmQAAmQAAmQgK0ToEiy9TeA4ycBEiABEiABEiABEiABErAgQJFkgYMnJEACJHDxCKSnp2PRokU4deoUDAbDxbsRWyYBEiABEiABErggAhRJF4SPlUmABEig5gQ8PDzQvn17eHt7w87OruYVWZIESIAESIAESOCSErAzmUymS3pH3owESIAESIAESIAESIAESIAEGjABWpIa8MNh10iABEiABEiABEiABEiABC49AYqkS8+cdyQBEiABEiABEiABEiABEmjABCiSGvDDYddIgARIgARIgARIgARIgAQuPQGKpEvPnHckARIgARIgARIgARIgARJowAQcG3Df2LUqCBiMJpRo9Sgq00HSjvZ28HBxgoeLI+zpNasKcrxEAiRAAiRAAiRAAiRAAlUToEiqmk+DuyrOCAtKdUgv0CC3pAzFZ0WSg4M9PJwd4e/hgkAvN/i4OTe4vrNDJEACJEACJEACJEACJNAYCNAFeGN4Smf7KAIpp7gMJ7MLkZpfglKd5WaUsuuKi5MDwnzc0SrAG34eLo1odOwqCVz5BEpKSnD8+HFER0fDz88Pffr0gY+Pz5U/cI6QBEiABEiABBoZAVqSGtEDEwuSCKSk3GLoDMbzei4bXolwSsorhsy4c3L0haeL03nlmEECJHDpCJSWliIuLg7Hjh3D0aNHcejQISQkJGDYsGHo3LkzRdKlexS8EwmQAAmQAAnUmABFUo1RXd6CRpNJmWInFqSKBJJ577R6I9LyNfB2dUHrIIokczZMk8ClIKDT6RAfH4+YmBhFHB04cACxsbEwGo1o2bKlIpAGDRpEgXQpHgbvQQIkQAIkQAJ1IECRVAdol6OKRqtX1iBZT7GrrC8anQFZRRo0a+IJZ0c6MayME/NJoL4I6PV6xUIk0+lEHO3fvx9HjhyBVqtF8+bNMXDgQHTr1g3du3dHZGQkXFw4Hba+2LMdEiABEiABEqhvAhRJ9U30IrWXXVyGfI22xq2L5alEZ0CJVgdnxyvzY0yj0ShTl+RjlEftCYhVQw47Ozsl1L4F267h7u4OsQaFhISgsLAQ33//PQ4fPox9+/YhNTUV4eHh6NmzJ3r37o0ePXogKiqKwsi2XxmOngRIgARIoBERoEhqJA9Lo9OjTG/pqKG6roujB3EPfqUeIpJ27NiB//3vf5Cx8qgdAfmQl/UygYGB8PT0rF1llkZQUJCypkhEUlFREaZPn47i4mI4ODigadOminhq3bo1RExlZGTAYDDA399fCZIn4pQHCZAACZAACZBAwyRAkdQwn8t5vfJycYKbk+N5Hu3OK2iWIfslOTlcuVPtPDw8MGDAAOUDnyLJ7MHXMDl//nycOnUKt956Kzp06FDDWiymEhBh2axZM0XsiKe6+++/H6tWrVKm3JWVlSEtLU2xLolokrIipqS8hODgYEUsNWnSBBJEPEkZKcuDBEiABEiABEjg8hOgSLr8z6BGPfBxd4aPmxPyNGWoidHEwd4O7s6OcHO+ch+xrOno0qWLEmoEkYUsCMi0MPmYFy9rQ4YMsbjGk9oRcHNzw8cff6yIza+++griuKFv375o27YtcnJyFDEq1qQ9e/ZgzZo1iuVTRJFY8UQ0yZolsT6JWBLBpQZxD+7kROcrtXsaLE0CJEACJEACF07gyv2CvnA2DaoFd2cnNPFwRWZhKYq1+mr7JgIp2NvtirYkVQuBBUjgEhJwdnbG448/rgif2bNnY8WKFYpomjhxIgICApCdna1YmcT9d2JiopKWKY/i3GHLli2KYBXrqJSNiIhAixYtlFiElK+vryKcJBbhJD8QcLreJXy4vBUJkAAJkIDNEaBIaiSPXFYvBHm5obBUh1M5RVWuT3JxdECItzuCvdwayejYTRK4MgiIcBk7dqyyXuntt9/GG2+8gby8PNx5552KpUgET69evcoHm5+fj9OnT+PkyZM4ceKEIpzkXPZV2r17N2TdnQgisTCJIwhxHy7iSabrqYJJjcWaZW9/5U6vLYfGBAmQAAmQAAlcAgIUSZcAcn3dwsvVCa0CvJTmZL8kceZg7phBnWInAqlNkPcVPdWuvpiyHRK4GASuv/56Za2RiCSZhidC6d5774U4cnB0PPfPrliFJHTq1Km8GyKMkpOTFeEkey2JeBLLk4gn8Z6nOocQ4RQWFoZWrVop4kmm66mCydvbGxK4zqkcKxMkQAIkQAIkUCsC5/5vXatqLHy5CHi7OaNtsA983ZyRXqhBcZleWd8gTho8XBwR7HPGguTqxAXgl+sZ8b4kIARkvZwIpBkzZuC7775TpttNmDBByZepeZUdYhESMSVBXSsma5xkTZMIJhFOqngS5xBidRLveuK8RNYyiXASi5PsxSTrncQxhAgmLy+vcuHEdU6V0Wc+CZAACZAACZwhQJHUCN8EWW/UIsAL4U08UKo1QG80wtHBHiKMHDndphE+UXb5SiUga4tmzZqliJfFixcjKysLL7/8Mjp27FgrhwwiasRSJOG6665TcMk+V7m5uYrFSTawVcWTWJw2bNigrImSjWzFUmUunMTyJO7LxcokwkliCa6urlfqY+C4SIAESIAESKDWBCiSao2s4VQQQeTpyjUIDeeJsCckcD4Bse68+eabikMGEUqy1kgsPSJeLuSQ9Ucy5U6CbForh1iTZDqeTM8T4aQGcRaxfft2xbOe7I0l+zSJcBKveuKBT6xOsuZJHEeIYFJjsWrRQcSFPCXWJQESIAESaKwEKJIa65Njv0mABBoNAbHSTJ06FX369FEEiYiQi3GIoBGRI5YqCeohFiVZ5xQbG1sexPK0f/9+xbOerINSN8GVKXpt2rRBVFSU4iRCrE0iqqTPEkvgfk4qWcYkQAIkQAJXKgGKpCv1yXJcJEACDYqAWH7EocPlOGQNlFivJMi+WHIYDAZlndSxY8cU4RQTE6PER48eLfesJ2VCQ0PLLU5idRIBJQ4iVMEksVicxCEFrU6X4+nyniRAAiRAAheDAEXSxaDKNkmABEiggRMQa5CsTZLQv39/pbcyXU+cQIjFScSTBBFPYnU6dOiQ4pJcnEjIXk4yVU8EU7t27RSrk7SjCiYRTWI9E3FG4dTAXwR2jwRIgARIoEICFEkVYmEmCZAACdgeARE0Mr2uR48eSlAJyHQ9EUoimqKjo5WgTt379ddflY1wZY2VTNUTr3winNq3b6+sc5L2ZK8nEU2qcOJ+TipZxiRAAiRAAg2VAEVSQ30y7BcJkAAJNBACYhES0SNh9OjRSq/Eu5540lOFk0zTEwG1du1aLF++XBFOYlEShxCqxalDhw7Kvk4yXU+Ekxqkfa5zaiAPm90gARIgARJQCFAk8UUgARIgARKoNQGxBsmUOwnqOidpJDMzU5mid+TIEYhwklg8661evRpikZK1SyEhIYpwEtElwkmcRMh0PRFN4u5cRJME8413a91BViABEiABEiCBCyBAkXQB8FiVBEiABEjAkkBgYCAk9OvXr/xCQUGBMl1PBNPhw4cV4STxpk2bIGuc5BBX5mJxEuHUqVMnJZZ9pmSKnggnEUxqzHVO5WiZIAESIAESuEgEKJIuElg2SwIkQAIkcIaAt7c3unfvrgSViezXJNP1RCyJUwiJZbrerl27FOEk0/lkPZOscRJrkwgnCbKnk7gjF9FkHiicVLKMSYAESIAE6oMARVJ9UGQbJEACJEACtSIgFiKxHElQ1znp9XpkZGSUCycRTxJkE96ffvoJIpxkSp64Mpd9oDp37qwEsT7Jpr2yrsk80EFErR4JC5MACZAACZgRoEgyg8EkCZAACZDA5SMglqGwsDAlDB06VOmIuCXPz89XrEwHDhzAwYMHlbBq1SosWbJEEU4ihsRBhFiaunTpogQRUE2bNoVcsw6Xb4S8MwmQAAmQQGMhQJHUWJ4U+0kCJEACNkhAptGJN7y+ffsqQUWg0WgQFxcHEU779+9XYnEQIZ71RFjJIfs5iVjq2rVreZDpeyLGpF0RTxJzqp5KlTEJkAAJkIBKgCJJJcGYBEiABEig0RAQ9+LqdLt77rlH6bfBYFDWOYlokrBv3z5FPK1Zs6Z8XJ6enorFSYRTt27dlHVSsuZJNsKlYCrHxAQJkAAJ2DwBiiSbfwUIgARIgASuDAKyHkl1Sz5y5EjFoiRWpZycHAuLk1ifvvvuO8gaKDlknZNYmFThJLFM22vSpAn3b7oyXg2OggRIgARqTYAiqdbIWIEESIAESKChElCnzqmx9FNckg8aNAgDBw5U1jCJcCouLrZY5yQOImSq3sKFCxWLkggucUEu65xU0SSxrJmSPZxs5sjbi8UfrcDOkvZ4cNYYdLjoA1+Hl1rdg69CH8fixZNxbZgPnOrxnnn7FuHTeWtwssXdeP/pAQisx7bZFAmQwJVFgCLpynqeHA0JkAAJkIAVAXUanaxBUg8ROn369EGvXr0U4SRT9WSz2xMnTije9cRBhLgl37FjB1auXKlYpUQ4ieBq166dMtVPdRLRqlUrxS252vYVFZuMMOj3Y9ln7+CL//0HV49+HNM/mIIbaqsuSnOR9sc0RD22HJ79Z2Lz0ofRskJQRuhKS1G6YyZGRMXh8z3vYkzbYHhWWLaCzKO/4NkZH+Gbv2PQbMSzePmFp3Fnd9+zBfOQEHsAh1Z8j2VlJ2A0mjBryvUUShVgZBYJkABAkcS3gARIgARIwOYIiHBS3YWrg5f9l2Sdkqx1uuOOO5TpeDIlLyUlBUePHlVEkwgnSW/duhUirKQNHx8fiFASq5O6TioqKkpxS25u0VLv0/hiA/Rab3S85T48/dqjuM6/DiNw8YX/kJfx62N7MPT9Z9EnfDne3LQUEytWSoChDDe+9SxuCAmERy1uF71/A+Ki96KgwICjiSlIzMwFoIqkXGQlZyItvjUGPjQekx+9FjKUtL1L8PVbj+K3khF4/J0fMKFbLW7IoiRAAlcsAYqkK/bRcmAkQAIkQAK1JaAKJ/MpdSKCZD+nESNGKBvdinDKzs5GbGwsjhw5ooRjx45h0aJFmDdvnuI9TxxBiAtyEU4SZF8n2c9JpuuZW7Rq27/LV94eji5ucPNwg9M5g1zNu2NnByffIPSc9ATGvvUwFqX/ixfnrMPEDwZW2oazpzucHexhV2kJ6wtHsX/zKSQc64n73nwBTzw6GB09zWxQiXE4EhuNHcbjsF/wHG5a/pLyS7FBp0FJYT5KfQ/ht9/+xs3dhiHEumlokXd6K758eBxmx/giYOArWPH9eDQ7rxwzbJNAPP754EN89NFSHOh4Cx59Zipeu7m1baK4gkZNkXQFPUwOhQRIgARIoP4JqMJJHDyohzh1aNGihbLOSabp6XQ6FBYWIiEhQRFNYm2KiYnB6tWrlf2cnJycIBvoqtP1RDRJEM96sjmuXL/iDzsXeAbchCde7IZfP47Fzdf2rWLIndEi3AXOtcCStXElVh0+CK+7n8HY22/GVXYH8POMxTji2htjn74D/scP4tjRQgx86BNMnXEL2srdi45j48+f49WZu+HXczyee7Z/xdPvirKRsW4e3t6QhlJ3O3TreRXCKuz9erzSdgIWlGqhs7jeCZN/+h8m9m0Kv4rGdPB7THj1K/yziv+tLwAAIABJREFUN8miVu8H3sazk+7BNeEW2XU6KcuIwfqvnsLEr6IB+CC45f34dMPzqOopWNxoy1vo/fg8pOVqLLLPnQzGtJ+nY9y1rdHkXKaSOrbkebw9ZxHWa2/Cky8+iUk3d4SPVRnz06LYNZj/+Rt4a2kBOlx/N56f9QIGB5mXAMoyj2PjV5Px8JfRcPWPwKDpCzH3DmtQRhj16/BS5EP42WSC0aKJLnhm8XeY0CMQ3tZfw/u+xF0vfo2t0ekWNdSTqx96D89NHIc+TdWc5ghv6Qu/oDSkbfwXW9v1wvZrWqOPNQi1OONGQcD6tWgUnWYnzxEwyh+9yQR72fPDrua/t51rgSkSIAESIIHaEhBrkIgmc+EUFBSEZs2aKWudysrKlDVOJSUlynS96OhoxVGEWJz27t2Lf/75R5mqJ/VlHyjxrieCSaxNIp7EciXT/66sww6ObkG4etpyxE7SwivYtYrhOcPR0a7cipT65zRM/GAlopOb4t6PZmHS0I4IczOvHo21i9ZjP4bhnrEjcENrFySuWo41677E78f2Ij97P9yM2zC/oDnuHXAtrouIgLjfKIqNRm7KcZxuFo5Og65FP183OJg3ezZdXJCD7WsXoUDrAl//qzB8UGuL9Qomgw4bXm2HiQtLkHEiHYUmE87s1qU2loF37roGc656Al9/NgHXtWwCVXIf/O4+vDr3L6w+mI+SsjMeF9VamZ9MwdbDMXjzuUfw0LXWAkAtVXWcG78Vi9+7F+/+o0VJXgbS87Uychhc81FWdVXLq2V5SElORnJ2sWV++VkWCkr1MJSfn0u0GjYN04wOKJi9CG/P0MFgnILHRnaqUCgVxqzGvE9ewas/5qDNwAl49v2HcU0FUzxNRh00eemK23+3MkdkFVve2VBWhLWvdcWji0qQdSoNBee6czaVgTdv64XPrnoKP3zxMPo09VHeCbmYlnAEpxPicPp03nm1JCNz9tPYsmYLnp/+OCbc1AHecESbawaj/zXbsOngZsTE7sPeYznocw1VUoUAG0kmRVIjeVDm3dQZjMguKsXp3GLklpTBYDTB0cEOfu4uiPDzRICnCxzMFiib12WaBEiABEjg4hCQ9UcyTU+C7Mckh3jSE+HUvXt3iHAqFacEpaXIyspSpuuJeBKLkziM2LJli+JZT4STCCRxZ64KJ3EWIQLK37+Cr8WLM5yL06qdPZx9m6Kltw76Da+g3WOLre5TgszMfJTgCOY+MAC/uDnCwQ7QFaQhOasQZbpYHIzLQsF1sBBJx5bMwS+b49Hq5nvQv2ck3BzjcWR7LI7tL0SzG3uh37j7EXTaBWm7FmDTf0ajy9tnPBQatcXIz85ASMeb0b9P93LhYtmpIhTk7sS634oBlyZw6zYMg1qf+3wyaDXY8EY33P/5CSTlS83b8enuV3GDp8tZIbUDs4c8h/mnTyFxzXt44L1grHjlNnRt6qXcpiw/DSkegzH12wkY2SMC7gCyt8zBzE/nY9WBLKSu+h1rruqJ3p3D0cnbsmfVnmlScGrvH/jhTz1c+0/Em2O1mDfmLfxdbcWqCnTEo999jgd7hsDHwirmicCIwAqFj7NXINrdPA1v2TvhlY9/xgczTTAap+CJUZ0ty6fswJ8//RczfspD1KBHMGPuBAwI8ankuVTeR11JHta+0RsPf3ECyfnyA/JYzD30Gvo7OZwVwdswq/8ULEhJRELO27j7nVCsfmUk2gWf+WHCoNcrP0DLHSLHzsSMCSPQq5k8mWNY+p/38b+lW3Fi5zysXNMVvbp0wHXhgFNwGJqGhCEEehw4Ho/9B2KQc03f86xqlfeaVxoagXN/5Q2tZ+xPhQTkV6a4zHxFIGn1RuiNZ4zH8k9AUakemYWlaOrrgSj5R8Wxot/DKmyWmSRAAiRAAheBgAgnmUonQRVOchuZqidrlUQwaTQaJc7Pz1fEklibVOEk65xkKp9snishNDQU/fv3x8svv9wA9nAqwek9y/HtGzPxt7E7bn5qFqYPsZoTZc7UoEFh9E94ZNxcxLjejk/WPoolS+40LwEkL8aT4z/C1pwBePrLabgxwg9iMEr76xVM/uQfxHiMxqBeEQg1m6uVtekzzPpqOWIi78J/RvRHV38nnFj1I35ftxIH247Ck2PvwMgekXDs/AQ69rwLRSVaiC0FSMOBZT/i05f/RKl3a7RyT8CGubMxeU4conrfh1e/uQ9dpJhWA01yPKILAXi5wKl9FNqo4sCohe7kAsyeE1sukD7Z/SHu7t4MfvaqJSwcM9bYw37Y85ifkI7kn7/HspHXICzAC4EuQKd7v8LPo5zhFxQAX3dnyJIvfdM7cPPa/Yg9sBEnNMXQlGhQamkoseRW2ZlLINoOeQo/rH0Idj4uMKUsw2+Vla1xviv8wyPRum04mtTCG76DexNEDX8Gb5jsMOPTn/Hx2/IjwjOYPLrLGaGUvB0LvnwfL8zdj7C+d+GJ1x9C/9DaCyQYNCiN/wWffXUcyfkOsHcYg//u+xDjOobB2059Jk0xc6Md7G94FgtSspH00zdYOrovHvHzOG9Mzj7BaNaqLaIiRUA1x6NPJuBQRipObDmJrQcO40B8Mq4Lbwo4tELrTq3QtjOwJ/o4Tuw/gGM5fUFjUo1frAZXkCKpwT2SyjtUotXjeGY+ErILIQLJ/BCzvgimojIjEnMKIYb+9iG+cKZQMsfENAmQAAk0CAKyzkmsReZT6oxGo2I5uuGGGxThJOJJ9nM6deqUIppEOJ08eVKxQImF6tIeWdi/cjYeO7QAXuVfDkaUFeUg/fRp5OEk0gplqthsM6FUguyEVZh125tnLBfiTlyTgbiYNJQ5Z+LTFfdh0V2dLIeRuwCnYYKufWf06twZXcJ9FSuCZmUm7I2lQGRHtAn0hlf5b4BZOLR5PY4cSUK8Zh5ei/4LH7k6oDQ3CakOzdE6KB27/3sfBsxSJ7eZ304L59BWaHbPbXA4MA/Pj/sD3v6u8GrXAYUrv8Br74Thi/8Mhl9hPk7s2IiDADzd3TB8wDXl07L02lJs+e5VrBUnerBDl+eexrguEWYCSfI9ENx2FG665Q38+UM68gt24J/txzGmZzMEBrnAzb8FIq0MhI6e3vByda3WgpKycwF++GIWFp/qgkHjHsWLD/dRPPaVj9LeCW6+YWitOPjLQ2JK+ZXLknD0CEC74U/hNdjhjc9+wSfvnhVKVxdh+Rcf4j9f7EVgr7F47q1nMbKjH6qakFnZALSaImz+YSbW5QL2jo7o9tzTGN+pKXwsViR4IiTqNoy45RUsm5+DovytWLk1Dnd0C0MT/6qUnyt8/X3g4XXmfSopLUOp9ozkBpwRGBqBkKahwKEkZGXGIykToCmpsifV8PPL/6lr+F1lD9MLSpCSV3yeQLImU6YzIL1Ao0y/a9bEzLOPdUGekwAJkAAJNBgCss5JtRipnRIxJFPt+vXrB1nfJEF1JKGWuTSxD1r3vh0Tp9yNXn7n37E07Sg2rf8T66OTzESSC7xDrsOEL77AaACG0nzE/PIMHozLgUvXuzFx4PlWp13rlyJPq0HU0GvRxt2tXIxotRplPyvx5OBkb6dYW870wgfd730HX984DcWKp4R4/P3+5/jhVyd0nfQQnnxoMNq5lKBY+Y4tRMw/P+LbuT/gUOCNuGviM3h4YAt4e7rDLu92bP17CX7+IRbNJk7BlBedofMPhwy1rKQIiTF7oIEL/F3ao2uU+v9VcQpwGkd2pEBZpWMHdO5zFXzsK/LI54n+Q4fCc2k6UFCAfet2IG5cT0QGBVUghLKx49uv8OumHUiQQXbpg249O6H9mdl5FvC1hRlIit2HfXGuaJVeCMsVTRZFG8yJo2cg2t/4BF6GHew/X4edi+ZixtoMLF20FwG9x+LZ16fg1s7BZmK8Nl0Xd/WnEb0rBSWAsvRAnom3hUBS2/PC9cOHw+P3+UBRMfas2Y74cd3RrJoprTk5mSgqErMiEObvh0Cfcw/Gr0kwmgSIb8RU5ORlIzUjF4iq4A9G7QLjBk2AIqlBP55zndNo9cguLkOJtnp7u/y+WFSmQ1pBCZr6unN90jmMTJEACZBAoyIg0/XEK54E8ah3+Q4nePpHIKp7b/Q+X9vAWNYB7a+6BiPtQs266AAn10BE9g5EpMx2KM6G42aZsmQHe68ItAq2tu7swt+/pKGspC2GDuoEPw/ns44b0pGerIW2DGgVGgRPV3P7ghN8w9vC96xPgxOrliM++SgCRj2Chx+8B8N6BMBTvnQKjmLVj79gwY8J8O0wEkPCi3Fo3y6k3jgM3Ztl4VhcHDZ+uxo7TxUgZen32Nfrv7izxZmhaEwGlJXJJ7cb7Ow8cM7JoQEGw0nE7pFyMo2rE9q3doV9hR/kgJd3Ezg4nPnsKikoQqmsezlLS7y/ffzLdhxJkwxxN34Cp1Lz0fb2F/DwI4/g9l7h8GgwX2wx+OGZ0Vjj7QJH1R38dU/iv48PR7eImi2acvQKRscbH8N/4IIvvvsJP64uRfO+Y/HMa09jZNdQeKvTGc/yqXmkh0GXgNi9UsMe9nYd0b61S7kDEOt2vH38Ye9wxixZUlAIjcFQ/kysyyrnOTvw2+K12HMgQyQSOrWJQutm6j5cgGOTAIQGBEH+CtJz85GQJqYkiqQKWTaCzAbzJ9cIWF3WLuZrtCjQ6MoXElbXGXHmIOuXisv08HarynRcXUu8TgIkQAIkQAJVE7B38URgeNuK3WdXXfXc1d2r8EuKFiVthmFQJ194OKtq4xROHi6DpgjoEtEUHu4Wbu3K6+fu/hFffP0r9jQZhYcmPoCbu4lAOo0t336PH7/9HRtKAtDhxvswaaAemxZ/jD9W/Yz/aYuxv10hfpu/B+6BY/DW3JHo7NUEEeWO5LTQlWUiPVluIx73whFavomSCSZTKTRnjAoAvKF0Te12ec+qTxSlRuPg7i3YnmhZNmH7MvwV2R4dW9yBMF9rUWlZ9tKdFSHpyG5YOCuPScODa/fghbmTMeKqZpbOGCrpmGNeHHbt2o4VO08gJwcw7tmPHRuP4bpeYTWqX3GzZ59JkVy1A+y8zjyTigvXKDdx+Sw8ffBH+Lo5ANocJB4/ieTCQAx4YAoef3AEugSYfUo7OsFFgkjdnDzkpGYgD23LtzOu0Q1ZqMEQMHuyDaZP7EgFBHJKylBQqs57raBABVkGkwlag/o7VQUFmEUCJEACJEACDYJAKpZ/NR+nNaUYeP896BboiXKNhDKUFptgNLREi3A/eLip5guzjsevwpwvvsG8jM54YNIkjL8uEn6uQNKW3/HrwjVIazkKUyeNQPPsPdjx5884lBaK3l0KsO6Pr7FjVyQietyJl164A327hsPSFmKE0ViKUmVrINlqwx1u5oYssy5cSLLtqDfxSfdnkK9uQXTyH3z01UKs2hOLDd+/CYNeB9Mj4xGVtQTz5y/Av8fP3E2TcwqJ8QBKjmLTdy/izvUfnJmi6NUS3W58ANMm9rVco3Qhnew2ET8vGY5SndmMlsy1+Oilb7AhIR77s77Ha99EoYX/GFzT3MyzRkX3PL0J337xCd798SiCr3kMj1zjgqN//orFn8yGkyPw9NM3oGVF9S5DXklqLPanxprdOQzXPzgFT0+5Dze0C7ScFujlBR9vH2UZUoJOD71Wd9ZJiFl1JhsNAYqkRvKoPF2c4ObsCJ2m5kJJjP8O3DupkTxhdpMESIAEbJdA6vJ38caKkyi+7gW8NKYdQt0dz02RysxAqk6LMvjC1dURZ2dHnYMVvxLvf/QJ5i7bjXRjIJZ/Ph2Hfj5jbSpKi4Fd8+EY9/AtaJW6Dr98+BW2u0Ri8OjRiExfjtj1e5CicUPzbia4R1gLpHO3qDhlD3uHEIRHAYipuIR5bkZ6CnT6s/8PDwmEv4tL+Zor74ju6B1hVrqwPVyhgeaTBdhw9AQ2r9mE/v9n7z7AojrWBo7/ty9L70URBATF3o0Vu9HEGDXF9MSY3suXepObm2bavaaaXo1GjdEYe+/G3lDBhtJ7W+qyLN9zFlBQFEwxgO+5D8+ePWfOnJnfrLn77syZGdAfd9VxoretZKV9iF+N9OSRfmw36ceqjnl2ojx0zMWtg1Qzu7r2PcIZEGVfgvfM2ZL2+BXkcNNrs4lOyODY9kOczDDTLcj1/JMuKAHS9Pd549t9ePS6joefe4BRYRoSQxzggxnM/O80ZfJ8Hn106B8IlDSotX60VIpZM645U+Jae+mpiVitVU9y+fvgrdedbpPqhIGjHuahiVF0MEYz88MfWLQtmdjN69kW1ZdOIT61gyT7TJb6c/Kozktem5aABElNpL08TAZcjTryGxgkKWOijTo1joY/PLC3ichIMUVABERABJq0QOpi3nllDvtcJvHWy/fSO9iErmZnUXwcB0tLKMQXL08D+rNHkHv5464uxVTmwoCrb2bi6J4EKUvaVG3FKbvZ+9PTfLuhDI+IsTz41BC8Y3fy6xc7sC9rZEnj8M6V/DhvIO2n9LiIycg0aDQhtFHmCY9V5pSNYfOuFB4MU6aDPnfMXfyJA1hKK7uKglsH4u5grHPhWnuxnVvSa0gU/ZduZv+hHHKOJZIUn4bD9Tfx+Hs9mFi1xmn6/t+YP+srlqa05YqrJnHvxM6VQ7v0rni3ansRdanWushXYwAdo3rS4eMlnEjIpai4hJLy8joXlLXnnLCBr6Z/wNTv9uGuBEjP3MdV3QLt0267j7ybp5Rnuz6Ywaz/vq/ESTz62MUGSlq02mDClEkTjyi9gJVt8lCo/5mgu0YVTx7fT5nysBvQOqQVbkZjjUlBKhM6BXakT9RIBrbsR6i2jOw3v2bpvrV8/3UYrVr5c1tUa04v+1xQQL45H/tkhzXuI7tNU0CCpCbSbiaDFjeTgXRzCaXWGl3d5ym/UafFy8mI/vRTledJKIdFQAREQARE4B8RUKYZ2ssXd7/ErIKRvPHeU9zRuxUOtSIkiNmxilRzHtZePens54bb6em/qwrt2par73+d0BH5OLbpTNtQ/8pFTs2HWTFrLnN+mEuyZ3sG3Hcv10Z5UJG4ge9nrGS3zYVgvwqKTcG0Co1nwQ+f4Bv4Iq+MCqmhoUOn98Lb/hySFas1mTTlWXxvJYkKjdaFvlFDYO4aqMhm3YKNJE+8niCNpvaX8oy1LFiWRG6+8v/fvRk3PBI/7wuP2zNUVFBSUYE9rHIyYjAZ8PBrh69fO7pVlfCkUxx7lZVhs90JaHcFo64Zjm+N0l+S3aICcsvLsU8u6OOJh9FQx4x9QPwGvvz0A97+br89QHro6Xu5qnur0+sS6Vxb0HHEXdxXmEveu18yS+lQ4mIDJRV6gzt9Bg6CX9ZjK89i7YJNpF53HX7KI0o1QdJXM29xCuZCpU36MmFUJF4eF3juy+BLxKihDJ6/hv37tpC0cyPbd45gSM/WRFRHSRYLFktpZQ+esyMmd9c/8XxVzcLK/j8hUPO3mn/i/nLPBgqoVSr8XBzwdTGiOd/UOVV5aTVqvJ2MBLhW/6tt4E0kmQiIgAiIgAj8HQKWAsw7Z/LyzxAWUkZ57A8sXLKEl296nHeWunHzO09x+4hIPHQaMte+ywNTbuOmm26y/z38ye8kZpfSY2hfQrzc6xjKZMQvsi9Drh5F77ZVAZJSB72FrNjdJBYF03viEzxyfQgVhxbxwb8+ZlupIx2GDSXcSYV7aGcGjRhCu9Tf+HLae0xddqKGgLIYqQMm+6zfxVjKDnDcPi93ZRK11kCLUfdzSxvlfQVZa9/hgf8s4VTpmZnryFjH+8+8weydWeRbIOiqSVzdtTVeRiXaO8bSN55n6pdriUmv7NGw55y3n19nLmTdjjhKlJisXShhIYF//zxptjLKdn1+2v6lxYmUWpVgNp0179zD6wtjyS2u8UNt1mY+/+BXohOy7UFS164daeHpzjm/wNcIkNx6TeRBJUDqEXQ6QKoG17m1pPuYB3jswUn0dFzHrGnv8/601cRVJ2jAq9pgInDkPUwKgwqblYzVb3PPv5eSXG5TOqcqt/Q1vPfka8zdm2ufOj74mlu4pnMgHoZ6vha7dmH40B60b+MKhbGs2vI722OUme6qtsJ8sgvyUeJoNw83Wvh71x0wVqeX10YtcM7nuFGX9jIvnDJLXai3C7YKSM0rwqrsnLXpNGp7MBXm44KjQZr3LB55KwIiIAIicEkFSjGnb+PHxz9hdVoMm/1v5sd7JrN62gNM//d/yM1tw5NfPcCkUZF4aCvXF3Jo2Z0hw/xJWP0BU2fvJT3fQqsrH+Oxsd0I8jh7rF1VZfL2s/C7X1i5K4780/XLI363mYAhkxgcUcimb1/hkyWpOLa7nqfuGoRz4k5mLy8DBw/a9R1G0Mn9vPj1HD7MPsnJNb25YuLD3N7LHZ3OiJd3K6UrBEtZJkdPZkJPr8q7qLXoggbzzHuPkTh2Gusz97D081d4OG427tqq3qSCo6xfvpvEwlL7MLR8ZRKGCltVr0YWseuWMfPUOtYtaY1v9Uq9xckc2L6XmOR8aDOEcdeNZXRn3zoCxNOVPc+OmZRD65n91lz2YKEg6ziVjzMVkpu2kDdvj+cbrQNO7W/k309E4WktY9+id5g1q/LhJm//u/m/Yf4YtIUc3zCfr+cmsO8nrzO9fYUn2LJ6D2n5JbQcfj8Pju1NG69ze2MS9i1l2cr9uPW+jgefmlJngFRdAZNPGH0nPm3v9pn20SxWLQmm37ihtK6akr063Xlf1XocQofy9FsPkzThQzak72LR9Jd58MQsXNTK09qA+Qhrl+4iuaSsqk0sNGyBZjfaDuhDl4Vr2HY0msRNO9m79wQju/ugzIxfmJ1Jemaafd2sNm6utPS1dzmet6hyonELyLfoxt0+tUqn9CZ5OBpp66fGw9FAZkGJfVrwcpsNpffIxajH29lo70VyddCjrK8hmwiIgAiIgAj8vQJmkg9u4Of/zWc/Lvi1HsYdL4wmDCuFWTEsfudF3p2zi/TOt/DqxzczuocXHdzAa/q7vLj2ODs2fMWRTUd44MOb6Oygx6nNYCa2gYJu/rSMSiC/2IpHx+EM7uiDcx0xUvz6T5m9YAGz5+8mw3cAV48eTNeWVdOED7RRkHCKxa9/z16LC4HD7ubeCQPo1R72z1hDbroJYzcfAiO7EXLXo6Rb5/LrtnzyE7cwe3kUY3sNxMXJhdDuA2jFjySVlXHA3pVUFSQpaz5pPGg/5nFe+7IDsdEzuO+jDSyaWXtmhaBevWgdHU18URE527/nzWf28INbGCPuHku3h16idPZLfL58JsuzarZUOENuvYuR11/D6G4dCfE8t/Je7YZz87Nf0i3fl+D2kWfNzKfkVUJeygHWfP89v9XMGgtF+ftZ8f1+0LviObw7Tz4RhZfyPcNTWeWnMkhyNilrQykXejHwkWmcfP8hPpqfS77SvVW9tRzBfY+PJGrEOIZ2DMCtjlGEjm1HM+XZbuhCetK5fTB1VKU6N/urPVCa8DSuwb2IK29Nh+r1e2ulOt8bFRqdN52ufoLXvuhAzIEfufeDDfw6Y0etC4L79KH13r2cKikh+/dvePWpHbRwCWPU/ffQ3j52sFby02+MQQOJuuIXNuyO5vfknfy+fTt7h3ZiRLCJ7MxksjKU+eLd8HALwL+eIZWnM5WdRikgQVKjbJbzF0oJlNxNBow6DXqNGo2qhLLycgxaDT7ODvi7mez7589BzoiACIiACIiAIpDP4aWz+G3tXuJOr/VzlkxpOhlFatpEdUYbN4/3/+84885ZpqiYnIQUEo5qiBhbyqbPPyI70JvXbuuGo96D0D438dQnd+IQ2IfxvQPsD8YHDbibB9198YxK5dCsh5i+aQuJFZsJ0mvrnsxg505W1CpaB656cBwDOrTA5B1KRK9x3B0xDtew3vTp1p7WHhaOrZvP4uVLWLYyA7VfB6656wo8shJZ8dErzMJMyuH97HXwo1eHSMJdXPBqP4zbn2pDz2NFuLvlElfa0t5zozE54tWuE+34kXiLhbSjJ8miR42ptVWo1K3oN3ky/RLCsUUexnrW8hveYWFoEtbw3Yufsyr5AKvnHQA64jx4HE9fO452IXrCR8eTVqsdAugwsCedI/1xrlX3M2+cAtrTV/k7c+isPWf82o7igenujD7rzOm3GgPGln1Qwj6VVk/QVa8yXXvYftr3irYY7M82OxMx/GYecLYRMb6QopoT7Xp2ZsTQzrTyMJ07zK7qJh5tBjDSPiTx9F3r3VECpZ5XhtHzPCl1zv50mvAC00Oz0Dq4ENSn5mLLajS6YAZMvpt+8RHYImPO6SnyCQ9HHbecr5/7jNUZ+1g5dx/QCbdR19G18yT+75UepOVZcI/sTxvvGgGqQwt6T3yIl8KGciobnIO7EuyoDJ1MJ/5oHHGHC8C1KwGhkYRJR9J5Wq9pHJYgqWm00+lS2ioqyC0qJSWviOwiC8UWq32BWWXdAmVNpKIyK34uJntP0+mLZEcEREAEREAEzhHQ4ugdSEiEBYeic05WHYjEqWUogR4mrKcOcNQ+HVwdaSOd8Jrcjm59DBwP209ugCs6NBicA+k54b5zvuiq1BrcOo3jvk4Q39ZG24PlnBVX1HGTmoda2UdNKF9iXCKHMzZyeM2TQBJH1m8j+pCO9tfdw4ionvTq4kj6rh2o009wdPVWft1YSMTIuxg7oYs9QEDngl9IR/yq5m3oWp2jxgkn38707ArL9xdTuncrO7MmMtKzOkGN18AB3D1lQI0DNXcjCTBEMCZrHwvf+5F1NRaO9e4wmmuVGdn+8s2IW4uujLrvdG0ufAeVBk3wIO67b1Cd6Vr0uZXb+tR56pIf1JjcCe5zHfddqDwqNeqgQdx7b931gQj89BFclbufX6fOYENyZTWcQgZwbfvztSN4hA/iyvCz8sw4QvShI+xNB9cuobTr0oFW5/ygcMmZ5IZ/QkCCpD+Bd6kvVQIkZYjdiYx80sxAPEWUAAAgAElEQVTFWKy1F4rNLbaQXViCuaSMUG9nvJzkX+elbiO5nwiIgAg0HQETrXqMplWPhpY4ilENSBpxbwO/kFfl1SrqAR6IakDGF5XEhbCo67hzsA8hvdrhVzUEzK3vGMI7dKTLgP703ltMUPueDIysHjp3vhvocfZsy7Cbh/LtnvXkJqxn2a5sRo6o2XNxvmtrHg+g102304tkOnl2YkyqmvDuLXCv0UlRM7XsXwqBFvS59S76kEQnt47sydAT2dkPlz+wekpG7H4Oxe4jDVc6h7ajS4dWyLewS9GGf989JEj6+2z/8pzzii3EZZpJySvGaqsdIFXfrNRqs/cyKWOI9VqN/Tml6nPyKgIiIAIiIAKXh4An4YMGcdbSp5VVd2lF5yHKX8Ml9M4eRAy+kQEtVjMnL4F1y3eTPWLYH1yHSAmW7qBXw28vKf92ASVYmsyFOqUuXIQMjhw4SOz+NAjoS4defekj3UgXJmsCZyVIagKNpBRR6UVKyy+2/50vQKquSlm5jQxzCa4ORRIkVaPIqwiIgAiIgAj8UQGNCdfQIdzzyqtEZjjgGOJ1zqKjfzRrua45CKjxjhzO9Q/6098vkj79ehJYY0Hj5lDDy7EOEiQ1kVYvLLWSU1TaoIVklSopzyhlFZRS6lGOQXf2yntNpNJSTBEQAREQARFoFAIaHFxDiJr8In/5yMBGUT8pxJ8TUHoux9n//lw+cnVjEqhn1azGVNTLuyxZhSUow+0auik9T0qgpEzkIJsIiIAIiIAIiIAIiIAIiEDDBSRIarjVP5pSmaRBGUZ3MVsFFdjqWHD2YvKQtCIgAiIgAiIgAiIgAiJwuQlIkNREWtzZqMNRf3GjIzUqFXr7+gZNpJJSTBEQAREQAREQAREQARFoBAISJDWCRmhIEdwc9Lg46FEWk23IplWrcDTocLjIwKoheUsaERABERABERABERABEWjOAhIkNZHWVYIdL0djg3uTlADJz9UBrVqauIk0sRRTBERABERABERABESgkQjIN+hG0hANKYa3swMt3B1x0F142J2DTkOAqwlvWUy2IaySRgREQAREQAREQAREQARqCVz423atpPLmnxZwNGgJ9nJGGXGXkltEocVaazIHnUaNk0GLv6uJEC8XjDL19z/dZHJ/ERABERABERABERCBJiggQVITazRng44wb1fcHQykm4spKLXaF5rV2J9B0uLr7ICnkxGDVtZGamJNK8UVAREQAREQAREQARFoJAISJDWShriYYig9RMqwOz9Xk31x2XJbBcpEDUpgpFY3bGKHi7mfpBUBERABERABERABERCBy0lAgqQm3NpK75FJZq9rwi0oRRcBERABERABERABEWiMAjJxQ2NsFSmTCIiACIiACIiACIiACIjAPyYgQdI/Ri83FgEREAEREAEREAEREAERaIwCEiQ1xlaRMomACIiACIiACIiACIiACPxjAhIk/WP0cmMREAEREAEREAEREAEREIHGKCBBUmNsFSmTCIiACIiACIiACIiACIjAPyYgQdI/Ri83FgEREAEREAEREAEREAERaIwCMgV4Y2yVBpapAqioqKCiAlQq5U+FrJLUQDxJJgIiIAIiIAIiIAIiIALnEWimQZIFc0YmObmFqFy88PBwx1F3toCV4rwcstLNVDi54enjgUlzdprG+d5WUYHVVkFJWTnFFivlNhtajRoHnRZloVllYVklYJJNBERABERABERABERABETg4gWaxXA7izmd1LRM8ovLsNkNjrDolXu5KjycsY98wNLDxZSWFpCdlkhyRg6FZYD1FFu+f5GJ4b256o7XmX8oA7Pl4gEv9RVKgJRXbCE2NYfNx1LZcDSFzcfT2HAkxf7+SFou+SVlKOlkEwEREAEREAEREAEREAERuHiBZhEkHZn3JLfd+Sj/m7+F+LwiLOVnIA4tWsfqNVvYu30G/76tD8PvepHZu/MojDtJbEw028gmZsOX/O/lf/PrkTPXNcY9JfDJNJewPzGbmNQ8CkqVaK9yU0Iic2kZh1Ny2ZuQRW6RxT4Ur/q8vIqACIiACIiACIiACIiACDRMoFkESR1G38Yg4xE+uy2Km5/5hnUnSrFWdinRddJoxo5uT2n0Pg7HZBC3fhbfffpf5uzOIiMtCdS+dL36OaZ+9jG3dGgY2j+VKqfIQmxaHunm4vMWQQmWMszFHEzOrhVEnfcCOSECIiACIiACIiACIiACIlBLoFkESfgMZ+zYdnQM07D1s//y5U9Lic6vrqeNssMbWLZmBxu0I7l72lJ++eZlojxTSEs+hcq3NYFde9LJuzp943xVepFS84rILCypt4BKoKQMyUvMKao3rSQQAREQAREQAREQAREQARGoLdA8giSg45U3MyCyG76aeDJzssivCpIqymNYOGMeqxbquO6m27nnpq64lp/kxMFD7Nuqxq91K7r16ohPTZcKG+WWEoqKS7E2kkd78ost5BaVYi2v6iKrWd469ovLysksKKa0rMbYwzrSySEREAEREAEREAEREAEREIHaAk07SKqwYS2zYCktpdStHV06+BHYcwKD+nWjfQubfRKHtLVrWba1lLBnxtNFO59pNz/PnMWx7D90gC2E0z58OMP7u1Kq5FH9l7SX5VPvpNOgx5l70oqtEQRKucUW+zNHtZvvwu8s5TYKLWeeW7pwajkrAiIgAiIgAiIgAiIgAiKgCKgqlIV2muqW/CvP3PYvvl57kOwadahcO6iqWsraQTXXD7KvK1RB5dmqc9WzZdc8p1KhVrkS3PUGXp//KTcG1rjBP7B7MCmHI+m59qm/G3p7D0cDnVt64uVkbOglku4yEnj44YfZs2cPL7/8MsOHD7+Mai5VFQEREAEREAEREIELCzTtnqSAsby5Yi+pFguW03+7+HbySCKr6t37zqn8FnPmfMziN5jcp+pku1Hc8fXuqmuzObRyGne2Bdzb0ffh79i07QOuct/EtB+3XljxEpz1djHi7mi4qDupVSr0mqbdxBdVYUksAiIgAiIgAiIgAiIgAn+BQBP/Bq1CrVaj0Wiq/rJY9+5UvlyygsNVODu+e4HxHaOYMnU5cRoNYeGdaduhd9XZmter0ajV2EGcHDEFR9C72028u3gXG56qTv8XiP/BLNxMBlyMtVfEVYYBKkPqSqzllJXbak35rVGrcDbq7H9/8JZymQiIgAiIgAiIgAiIgAhclgLa5lTr6B+e538/zGOLMYgg3yJK0tLQtwjAaj3FjFcnMmfGCAZ30mBI2llZ7Zw88lPSSQd8KCA/O43EI+AW4Ui7VgGo1RrUek2jIFJ6hDwcjaSbS8guLMVcUmafwU4JkJRNpQKDVoObgx5nox5XBz3+Lg72oYaNogJSCBEQAREQAREQAREQARFoIgLNJkg68P1knnt3BitKr+HZ18fgtWYuX36xFMeoKdzdz5kdc97hi7VLWHYcVL5XcMUod3zzT5B/PIU0JUgqMJOflky8DZwdTQS3DGh0TdjCzZHMghKOZeTbAyX7s1fVpayAYouVkrJyym0VtPNzxd/VsfqsvIqACIiACIiACIiACIiACDRQoIkPt6us5YHv7uLZd3/keOTzfPv9+zx3bVd89Br70DmVzkDAwHt47+d9xC55j3sHd6HXoGu4fVIUAZps8vOSSMsACvLJTkkiVslSmbRB3fhoiixW+5TeOrUKrVpVNfnEmZZWJqNQjitD7QpKy+zD8M6clT0REAEREAEREAEREAEREIGGCDTxnqR9fHvni3y0YDVut0/jf/feQlQbRwp//5k1h/ZTOOoRHrrtJoaGOWAsOMySDes5aOvA2Ftu4BrvraT6GMnNP0FyGhQa8khLSaAcNxwd2tDSryF8ly6N0muUml9EVmEpyvNJjnodhRYrRWVl9p4j5Xkqk06Lo0GLTqMmv6SM5NxCwnxcL10h5U4iIAIiIAIiIAIiIAIi0AwEmnSQlLZiHvP2ODBg6lzuuj6KcBdHDJpMduzcxfFjScRnfcd0Hz+83B5gfIf2jHnyYwZatDi6ueOU4om3jx/HNiSyeX8MIyKySEk6Cm5heHe6gu6NbLRdYamVnCILFms5yqx1Bp0GnVaNa4UeZUJzlfI/FfZzyudS6XVKNxfTysMZvbbx9Yo1g387UgUREAEREAEREAEREIFmKtCkgyTPvo/w6a9WjJ7uuDoZsFcm6xC795zieEo5foNv4q5bbmCYz1Hmvj6dj1ZWMPC+B3niJk/UgUGEt+1A16+2cmrDRnb4lZCSUAYuzjiGBBHYOOZrOP2xy1EWky221BpipwRL2Nd4ql7o6XRylNWvSsoqF5PVay9u6vAzucieCIiACIiACIiACIiACFx+Ak06SNKeXMYbr3/Luv0pZ1rOWkhmajrZVlD9Ppe37lvDp7pScjMyyTBXcECjx8HTnedHeuAV4E8rvyMcOjiHhQtC2HkUAjp5M7BH58qA60yu//ieucRiH153MQVRepiUSRxkEwEREAEREAEREAEREAERaLhAkw6SCBnFU//pzj1FlqoaH+G3l//L10dOEXD9czxwxw30Cshl388f89kXc0kOHcW46ydxR+8WyhQHtAxuR2RXX35buYXkg7sotrSgo+8A+nV3aLjgJUpZObW3zj6rXUNvqfQ0KRM5yCYCIiACIiACIiACIiACItBwgab9sIrJi9Zt2tG5c2f7n+nUNqITDpHS9QbG33gjNwztTGfXUlJTUziY40uf3sOYcGVvAtyU4Wca3IPC6NCpKz5FReTm5FDaogU+/a+gu6nhgJcqpbvjuYvJXujeygx3Jr0Wp7MWoL3QNXJOBERABERABERABERABERA6U5pLlv2VpbN3862w/mU6NbzzdyOhAcW4rPuF5bN34Ox27VcM34sA1saT9dY4xFEaIdOdAtcSEKCG23CejP+6u40xtWFjFqNfTHZDHNJg4bdKQGSr4sD2kY4lfnpBpAdERABERABERABERABEWiEAs0nSHKKYOxT79PzTjOWuBVM+2kJL0z+CUN2KvFZhZR5F5FjLsRWo8aFpw6yc/NatmYqLVNIcuYJdhxMYnL78EbXVCqVCn9XE+aSMk5lmbGU285bRmXmuwBXE36ujbBL7LyllhMiIAIiIAIiIAIiIAIi0DgEaoQMjaNAf7gUeg+C2nsQpGQQWsiGpavZsCyaLF9ffLy8qDi5npmvxXNs703ccttdjDDtY+7nU3l79m70ni3p6u3NnpjN/PLOvzDlPMoL9/bF8w8X5u+50EGnJdTbBeUxo1PZBZSUlZ9zIyVNkKcTIV7OKL1PsomACIiACIiACIiACIiACFycQPMJkqrqnbv7J6Z//Q0zNsYSOPpenrhtEv18K0ha/yVfz/yRJQv90Vmy2Ve8iJm/FePW5UEeeWEswZmHmf3pq3yydgnfTT1GwoGRTHr4DcZHXBzo35lamfHbyaCzLxDr5WQks6CE3GIL1nKbfQFZN5MebycHXBz09ueR/s6ySN4iIAIiIAIiIAIiIAIi0FwFmk2QlLPrRz75cSkbtu4iy6cf45+9izGDehIeFoK3CQrb+RM5ZBALZ65hxewv2eDfis43P8bLtw4lsnNLTAVtaeWuxt3jE17/aTeLZiRwaF8037YOpM3Yh3lvYttG8RlQAiXleSOjToO7yUCptRxllm+1WoVBq8ag1ZxeULZRFFgKIQIiIAIiIAIiIAIiIAJNTKCJB0nZ7Pj+fWatjib22CFSvQdy5aQXGdCnBx0jQghw1Z1ujvyEvWz47WdWbCrFpdctPPvIeAZGtKdLoEtlGidvgvtM5EFXD5w8PuOTT3ZRmONB3+eupU+oz+l8GsuOMr23g15r/2ssZZJyiIAIiIAIiIAIiIAIiEBzEGjSQVLOjp/5+oe1pLYZwvB7rie8bRe6tgvB3+VMcETyNn6aO58ffl5EsVsEvSc/wfioSDp2CaQqPDrTjkY3/DuOYMqTregyMAWVTzg9Bkc0umeTzhRY9kRABERABERABERABERABP5qgSYdJBn9ujPh0SCcwrsQGeKLS121cfKnbfeBTPLsgEdwRzp26kx151GdmFonPEN6MCqkzrNyUAREQAREQAREQAREQAREoJkL1BVWNJkqOwR2Z1hgPcV1aUWX/spfPenktAiIgAiIgAiIgAiIgAiIgAgAalEQAREQAREQAREQAREQAREQARE4IyBB0hkL2RMBERABERABERABERABERAB6UmSz4AIiIAIiIAIiIAIiIAIiIAI1BSQnqSaGrIvAiIgAiIgAiIgAiIgAiJw2Qs06YkbmlPrldts5JeUUVBShtVWgVoFTgYdLg56dJpzY9mychtmJX1pGeW2CrRqlT29s1GH9jzp84otFJaW2Reftac36nB10Mvis83pgyR1EQEREAEREAEREAER+NMCEiT9acI/n0F2YSnp5mKyCkootFix2mz2wMVRr8XdZMDP1WR/1ahV2CoqyCksJTW/mJyiUnvQowRJGrUaJ4MWD0cDPi4OeJiMqFTYA67colJScovILS6lwGKlQkmvUeNs0NnT+7ua7MHSn6+J5CACIiACIiACIiACIiACTV+gWQVJhSc2smz9CVRtrqB/z3B8DI2/gZTA6FhGPql5RVjKbbUKrPQUZRaUkldiIczbBQ9Hoz1AUtIrQZXSm1Rzyy+xkFlQgtJjFOZdgZvJYH9/ND2PdHOJPcCqlb7YYs9HuS7C100CpZo4si8CIiACIiACIiACInDZCjSrIKng6FpmffYTR916MuTKaxk/8gp6tvWlImkvm7dsZVemJ1369GVg15Y41NXkZTkkHtzG8uX7KPUNpufIG+jpX1fCv+aYMvTteGY+KXlF5wQ81XdQepVS84pRobIPrcswl5CaX2QfYledpuarEmgpvUwqsPdAxWcX2N/XTFNzXwm0EnMK7fl3DvRAr9HUPC37IiACIiACIiACIiACInDZCTSrIKmy9bLYv3w1Kpdw+gzqSQVQdGobC796kw+3megzdDij+7bGsa6mtmQSt2sNc3/eRklIb26gG+3uaINTXWn/gmNKcKT08JzdI3R21soQOyUwyiossQdHyvC6C21KfkqglK0Mx7NYL5TUfk7JLym3kAA3Ey3c6pSpNw9JIAIiIAIiIAIiIAIiIALNRaAZBklK04TQb1A/endpYe8xKqpurdxsMuNOEBugwqP6WM3XslxSMs2UYUCNKxoKqT/EqJlBw/eVwCTNXIylrLxBFxVZrJRYyzFoNBh19ff2mEvLKDZbcTLq7ZM61HcTpTzxWQUSJNUHJedFQAREQAREQAREQASavUCzCpIcTI7odRd6EKklXYdP5sWp42lbV9MWHGPjjLc4tC4Fm387hg7vgltd6f6CY0VlVgpLrZRXXLhXqPpWxZZyCixlqB1UGKk/SFKG3eWXlmHQadGq60+v9FZlFZVW305eRUAEREAEREAEREAEROCyFWhWQZKLiztGB1NVY5aQenADhw/kUZKTSF4ja+Lycpt9KGBDi6U8m2RVrmlYTEVFRQXW8gr7a0PvYbNVplcp0+LJJgIiIAIiIAIiIAIiIAKXqUATDpKKSNi5kT3H08it7gBJ2MKxlMpwKHb7Yr7Zuo9NS5LRB7lSXl4IqDkVvZ5fvy9ge10NXprGkW1HycOCgzWPgtPj9OpK/OeOGe09PMp0DDQoWFKG2Cm9Tsr6SQ3Z1CoVeq0aZdrwhmxKKoNWjQRIDdGSNCIgAiIgAiIgAiIgAs1ZoAkHSaWkxWxj0/oYEpX4R9lyTpCUqbwpJ/7QIRzbtKTzYAPH9kdz5Fi2PRxJObGbjcsycAHKchM5ejyBLEMwPTr442A1k3EykQIqcFBV2NcZqsr5L39RAhIXo96+gKzSQ1TfpgRJWrUaraZhQY+DToNOY6hzIdq67qVWq/Bzqe6FqyuFHBMBERABERABERABERCBy0OgCQdJ7vS45SV63FKjofZ9zV1Pv82JVB+G3/Evnn4wimBzDIs/fotXp81mW1owfcY+zn+qnknKO7CATz7+nAV53bnqtmG0b+WDa+J6PvtiL/pOV3NFWI28/+JdpcempbujfUFYZZ2i+obRKQGVslhskfJsUmlZvaVR1khyddCjTBlear3w5BBK2OWg09LK4++ax6/e4koCERABERABERABERABEWg0Ak04SGqgoXNb+gzsQ691q9m2wYDOwYC+6lKdToehNIHtP23kxLGTTHntFd4YeR/TRjQw7z+ZzNvJaJ9NrizLRnGZ9byBktKL1MrdCV9XB9LyizmZZabkArPiKb1ILd0c7VN6K9cmZBeeN1BSHj9SAqQgTyfcTNUyf7JicrkIiIAIiIAIiIAIiIAINGGBZhwkFZByeBeJcWkkHY7mZGYxeLrg5ONmH2qntJnJ5IyrmydQSvs2g7huZIh9qF5xbjInDhwlW9+ajr1b/20z3Gk1atr4KAP/sC8oq/QQ2SdnqPpA6TRqewDTws1EsJczTgYdjnqtfRhgcm4RyrTg1WssKb1BSn6OBi0BriZCvV3tU4XrtRrUqEjJL0KZIU+ZAELZlPQ6rdqep7+riQhfN3keqcpdXkRABERABERABERABC5vgWYaJOWSELOCr1cfJzY6lQJTDilxmWDIIvHwDtavLKoMlDIOcCg+R1lulpzUQ/y+ciWZlJB2eB1z/vs90W6TePGLJxjfM/hvC5SUICbCzxUvJwPJeUWYS8rsC8YqPTzKEDs/Fwe8nIwo6ZTN0aAj3McVT0cjqXlFKEP1lLVlNSoVzkYd/m4m+zklwFI2JbBq5++Gp5PBvsBsQUmZfaIIZUIHFwe9PaBS8lcmepBNBERABERABERABERABEQAmlGQZCE7M5NC+5R0saycbSYkaCR3vPoKPQ3z+eK1N5gZm0D0mll8dKBqgoKSXFIS4+09SfEHl/DVG3twrv5UtI7AO/d3vnx4Ov4r3uLKyg6f6rN/6asyIYOviwkfZwestgrKbTbUajU6tarO3h0lYFJ6f5QASukZUuZ9UIIeJZ+6Yh0lfUt3p8qhfeU2lKm+NRp1gyd1+EsrK5mJgAiIgAiIgAiIgAiIQCMXaAZBkoWcU0eJTzvBmlmLiI5JAFyIHPsIrz4zhdFtDBycOxdrGegjR3DHi//mlatDK5slfgNf/e8l7p6WTOehT/G/mXfT9XSD5ZGwZxGz3ttEphJHdTh94m/bUSZz0GmUv8peoPpuVJlemcWuvpSV55X01T1SDbtCUomACIiACIiACIiACIjA5SfQhIOkQtJiT5Kad4o10//DZwu2kWJzwWIxAp3p07UrkW2U2dpSyUzJISsNWnT3INDHq4Gt7Epg15v5vxk3NzC9JBMBERABERABERABERABEWgOAk04SIpjzX+f5a1ZqzloBpeWYYwb2Jno/dHsjq7RNCWpJMWnk5ymx0ljpbgom6RcAy3clGDq3M1anEdmciJ5FT4EhnkjKwedayRHREAEREAEREAEREAERKA5CzThIKkDk157ghSrjl+2W4l69kUmhxzk1ZdrB0nlx2PZf+oEhx2CCI1ZxcdTj7Fz5EM8PaI1pMaRbF981kJhbjLHo6Mxx65lzrQ3WFM+mdd/fIyRrb0kUGrO/wKkbiIgAiIgAiIgAiIgAiJwlkATDpIA7yE88dUQnqiu1L6D1XtVr+WkpSaRmZ6OsWtfOgWrqdg2j++f38Jv73rh42Kwp4uIMGI+MZMXx39LcX42qWkF4DCPD18IpfXMO+lyVq7yVgREQAREQAREQAREQAREoPkKNO0gqb52KU8jZv8Bjh3JpeuVvRjc3cqe4/PANJxbnn+ND26MrJ1DwTE2zniLe++fh63bSB58RwKk2kDyTgREQAREQAREQAREQASav0DDplFrog5lKfvZu/cIx/N6Etm2O21bN9GKSLFFQAREQAREQAREQAREQAQumUDzDZJsFg6uXcT6nTswjhpA1LCehFwyVrmRCIiACIiACIiACIiACIhAUxVo4kGSDUtRPkUlFqw2sNlsVFTY7G1hTV3LqhV72HP8Cob0G0Lfbm5NtY2k3CIgAiIgAiIgAiIgAiIgApdQoIk/k5TOiv/czGr3uxg3/mpanIwjOzdXmdGB/WsWkF94gqA732Li+GH2XqTj1bA2K5biAvLz86uPVL4WFtgDLhsV9mDLVhlv1U4j70RABERABERABERABERABJq1QBMPkvxo0dqdfdOm8NnLVixlVsorQKXKZNs2NZr+9/L2xHEMtz+LpAQ+gPJ3eAlf3beC7x88uyOtApu1DAvlBBTHEJ8MBDbO9rdXpaKqTlVFVKmUuqtQNc4iS6lEQAREQAREQAREQAREoEkInB0lNIlC1yxk13t/Zs3hIoq2TefmIRFow9sQ6uuHf+h4nnt4CncODa5KnkN2ZjaZ6SrUHcfx4A97KCoqqv2XfoBVH02mg1aLVqNB3UijDavNRlp+Edvi0lm0/xTz98Sx+MApdsRlkJ5fTLl0gdX8iMi+CIiACIiACIiACIiACFyUQBPvSapR186T+XbFXXybvpK3H12NZcCV3HpdF855EqnTBB695T+8d127GhdX7TqF0f+eT9k35VP7AaVnprFthRYrMSk5nMwqwGbvGqssYanVRnxOAcl5RQR6ONKxhQcGraaxFV/KIwIiIAIiIAIiIAIiIAKNXqD5BElUDjXDZxhPzRiK0g1Uu5vMg563vsMvN1egUmsuMCRNRWMMjpRPUpESIKWeGyDV/JQpvUwJOYX2Q51aeKCXQKkmj+yLgAiIgAiIgAiIgAiIQL0CteOIepM3gQQqNRqtBo1afU4gpARHWq0WTWMdR1cPb3JuESm5xbV6kOq6xFpuI8NcQlJuUV2n5ZgIiIAIiIAIiIAIiIAIiMAFBJpfkHSByjblU0ovUnZRCcVl1gZVQ0mvPLdUblOmeJBNBERABERABERABERABESgoQISJDVU6iAW1acAACAASURBVB9Ol1dkwVxS1uBSKM8rFZeVYy6xNPgaSSgCIiACIiACIiACIiACIsBZj+2ISKMVyCosIa/44gIem60CqzInumwiIAIiIAIiIAIiIAIiIAINFpCepAZT/bMJ3UwGnI26iyqEMgGFRtMIp+i7qFpIYhEQAREQAREQAREQARG4tAISJF1a7z98N3eTARejvsHXK4vKOui1OBsuLrBq8A0koQiIgAiIgAiIgAiIgAg0UwEJkppIw5r0GpRAyahr2NpHSnpvJyNajTRxE2liKaYIiIAIiIAIiIAIiEAjEZBv0I2kIeorhtIz5O9qwtfFAXU9CzkpgZG3kwMt3Bzry1bOi4AIiIAIiIAIiIAIiIAInCXQrBaTPatuze6to0FLuI8bVEBibmGd03vrNGpauJlo6+eKUSfN2+w+BFIhERABERABERABERCBv11AvkX/7cR/3Q2UHiQXBx3tAzzwdTGRkldIdmEpVpsNJTjycDTae5s8HQ2Y9Drq6XD66womOYmACIiACIiACIiACIhAMxKQIKmJNaYSKCk9SgadCS8nA5ZyGxUV2AMivUaNQadBq5ZRlE2sWaW4IiACIiACIiACIiACjUhAgqRG1BgXUxQlENIa1MhTRxejJmlFQAREQAREQAREQAREoH4B6XKo30hSiIAIiIAIiIAIiIAIiIAIXEYCEiRdRo0tVRUBERABERABERABERABEahfQIKk+o0khQiIgAiIgAiIgAiIgAiIwGUkIEHSZdTYUlUREAEREAEREAEREAEREIH6BSRIqt9IUoiACIiACIiACIiACIiACFxGAhIkXUaNLVUVAREQAREQAREQAREQARGoX0CCpPqNJIUIiIAIiIAINBMBK2BrJnWRaoiACIjA3ycg6yT9fbZ/e87Wchsl1nLKbRVo1CqMWg1ajcS9fzu83EAEREAEmqxAGaAD5P8rmmwTSsFFQAQuiYDm3//+978vyZ3kJn+ZQHFZOWn5xZzKLiApt9C+n1FQQl6xxR4w6TQSLP1l2M04o6VLl5KamkpUVBShoaHNuKZSNREQgTMCSpCkadJBUt6+eXz29QpOaX3x83XHpFSn1lbAsXVzWbYjnlKXlgS4KkGhbGcESshP3c6Ct7/jZOBAwj3OnLm4veMsf/sjvolVEdrKBzdTtXM8G79YTGyxAy6+Hjj+mZ/j8+PZt+pHPpi/gwLXCNr6GmoV0VqQQczyL3h/diyOHbvQ0qHW6cv8TRyr/jedHcUt8PFxx1H/RzgyiV27kBlzfifBIYD2AU5VmeSRsGcr6xYcxBoRjk/tZrm4Gx2ey9NTv+KgtgNdA53QalS1rs/ZOYNpn60hyyOYAE8nDOf8e6+V/C99I0HSX8r592dWUFLGqWwzJzLz7cFRbrEFc2kZ+SVl5BWXYS4po9xmw9GgQye9Sn9/gzThO0iQ1IQbT4ouAn9YoLwqQGqqPUl57Pn+Fd74aC4bDh3H5hVBkL8PLjW/pKXuYOan/2PazFXsOVCA2sUD/xBvTH/Y7K+68AQr35vO9/Pj0LYLxMfVwd6n19DcC09uYfHc7/j6p92klzvh18YHx3ovtpCbuJdlX/yP2ZuPUN6iFyFuNgozjrL8f0/y7cEy1AEd6NLiD0QXuZv48NHX+XL9ARKOWfHsEIyPuwkdaWyaPo2ZKzcQU+FHoL8vXg2NlEqSiV63mlWbkzGEtsazLJ49y77jzXfnctxipHXr1lgPzOHDn7dga9ELP2sc6z+7n+e/2kFCfi4Wj45/rC71Ojb2BNls/+5dPp+9iCTnLgR5O2LUFrHzqxf5etV+EvQhhLf0wtXhIiOM4hNsmvUF702by+aj+Wj17vhHBuBMIQm7VzB72iesyrSi8wonoiGRkq2M0uSNfD51OZYuPWhlqoBTc3n0/ndZdzwdvy4jiMhfwdSPviPBsQutvU2c/OVxXpi2kG1HT5FtDKZNC09cjBdZjz/YfH8mvv+Dt5TL/qhAkcVqD5BOZhWg7J+9WW02sotK7UPwKiogxNsFo+7SfJDOLou8FwEREAERaIwCyq/9tX+pbYylPG+ZEjbxy4ZY4jOT8bTp0Wr0aNVmjqyez6rfM/DoN5p2BevZvm8/0ae8cQ8vQ+Ogtw8uLE7cw4a1K4khkqgRV9HZ97x3ufgTFTaytnzOf5fAqEfupq+PlrN+EAcKiV3+NZ+v17Il43c6BrhhvIg7FSXsZuPmLeyOc2SoWUtgjw74eNeTQVkeKfuX8eVb77LOFsLudH88X5hAhFMw7To58ObcL9g89mHuqCebuk7n7ttBdFEBRXFp5A1TU6FTV32yAug1thML//UFP76TRUHBszw8oX/DvkSrrGTHbWPxd1tZk5rMjUPaYXR0xlRYQkZqLrm5cZyc+ybvzK1gT6Y7D443sWdTMmXlJSRmqXA2NNXgvy7hiziWs5ufP/qQT/dm0HKfE4a3HmZ0uxZ06BlE2uvz2djpJq6L6kjgRWSpJC1OiufEqRMkZprxU757Ohrs/dDgTECbdkR2LOftj6eSmFaI6qEpXN3e5cJ3UFVgK89h3+w3+TUvgfE3P84UHw/8VSqOxqVTrK7g+Kr3+eT99bjvdMD09lWkrkugMD+Z/GwLWpUarfrS/fdLgqQLN2ejOVsBpJuLScgprDNAqllQJYBKzC3E2UFHoHt112jNFLIvAiIgAiJweQo05h/Octg541NWHC+h1C+KOyZdQSsXY9WXMqW1Etj89UyW70wiqxTcyizErPqR6b8XcGz9ItbtzCT0+gy62fZz6EgGVHhgtcSxc8X3HFwBxcn72bJxHUcJ53AW3H/TVXT2+fOfggqblcytX/HKq28wfQVsz4YXX5pMPz8d2lrf51xw89Wg1cWSlD6IDgFGjEYwH17Kz6v3cbIikjFX96dLsIGE9b+xZtthNB2uZnC/HrR2BaOHO85OTuDqiMndGadaedddj6L0U+xfv4AdGc6YAtsRFqpHGWSid3AitNsAfH7aRGJGNuBadwbnPZrL/jUbicszY2l3B7fffxV9Aj2o7I9yos2Qaxg8fzW75qxi1pwudO8UQbBPS2p2+NWZtcGHtr2602HVYqZPe52shHH0dQYcvHHzD8bFvJ1Zm7JxbT2G7q0sFKfFsTtGh6lle0ZPfogJnS+2HnWWoskdjFv5NWvizBRbO9ImzBlHo8r+W0jLTn0IctlGXE4eJZbq5xEbWr1iko8e5NDBI+T6dGLMuPHceGUHKv/J6PEI7kzf0aNo99trrF80k2/D+jOofW8uGCapdOh9e3Lz3RHc8uJUXj+eh+9DoZQrc8kERNHdfR8//7qHArf+jGznhnPJAZYcKqag2I/BE+/hxsHt8HU+X+iSxt5fl7Np93EyAnsycnA/+oa6N7SydaY7353qTCwH/zmBkjIrWQUlFJYqH/L6t8JSq304nr+rCa36Mv1lpX4mSSECIiACInBBgRxO/L6ZDSv3kFArnQm/sO5EXR1Fm7p+iyvNIz16FZ8tOVTrKq3BRPeJTzIipNbhyjcnlvPeW28y/7AZj+taM35871qJEjd/zfQ5qygMHMpdN7iQvGQVc9bPwr3fBAb3vIHJAwvISzrAzg3HUUdM4O4wOLTtN95Z40zPgQMY2LUjQ67vyBDA2wtQfn2s3izZxB/YwuIle8isPtbQVyVI2v4dCxnLcy/Arhlv8Xb3vvx0a3ucz/MARcTIyTx0Wy+U73DJ87PZsT+Wk7aujLr1IW4f7MzWV49wJPowup6jue2R+xgcBMT+Qll6LOtzfWgR3IYgpQ4X2spySTy4mYW/7qTIpz1j7nyGR+8bQCvlmnJnAroNo7fzMtbMn8Pu65+hm9uFMjvrXN5+1myII9/sQZ+HB9PBz70qQKpK5xROu7YeeLlDuZMjRsOZB2IyD61gT7oW3/C+dAo4uy/NiE/bXgwYMYTlq77g6M5dePdoCSjDBnexZsUpjgXdxYtTbueGQU7E/vAkhx1cce02lol/RYCUsIlvFm0nMbPwTIXDBnH94O5E+F1ocGMWR9ZvYOOGaJLPXFnHnjstI65g1PXd8S/K5OTe1fyw+kgd6ZRDehycezDhsaG0Pk8K++Hc3fz01UqOmkuIuPohHn/2Jq4IcKoMSNsPpG/ID+zYvIwdR3vTNqANbtWPjl0oT+VccRJHDx7iYGw5rXp2oc+g6gCp6kK9B14twmkfClvTdLi5KNFs5Vaal0LM7ws44X8T13aqGbiq0Oh96X3bvQz/ZCVfr/iV7ZMer5pvM4sds95mTtEwpjwymYfuHoDrnjd4o7SI4rZjuapna3zOGyAp960gfd9vzPzkZ7aaRpCHO61b9MP/7I9YdSEb8CpBUgOQGkOS3CKLfWIGW83/qF+gYMrQO+X5pWKLFWfjmf84XeASOSUCIiACIiACpwVyjm9h6+YF/DJnHasW7+DU6TPKjiP+Eb0ZcfwEI/sN4OrBbaiOlUrzkole8iEz5ixm2oIDta7SGh3pfRwOTBzPk8Nrf/U7ueILlscVU1ruz8ARPQg4PbQH8g8t4csPfmCvzxVcN/l5HrzKixinQp75YjFmv56MuXMKvVQr+X7qLpzaDmTEA49wY6dilnxYTMrs47TsMpRbHruZLuedpMBGeVkpxUVFFNUqccPemDrfzPNjnuCevrCtlTtL3NV1DLdrWF5/Vaqi9CPsXj2L1ck+tLpqAvdPrgqQlBtoHHAO7MV1I4P4dfZs3pszko/v6UJD46T8/avZcMKMOWQsd4/sQkv3s7+JOtMm6gbuVHdA3f4a+rbxsX9pVwKknz//DzMPaIm4+mkevn7ouYGSwYc2/cZx/3NepBvd0Zl3sREzaSePsjfJStAIR0qSf2fFlgCy128lx9WdkddMoGt14ctLKT6yivd/PYhGG8iQyZPo3oAOBSUIX/zLbN74YQvxGQVnmiF8GEePTOHRO4bTMcidur9RZRGz5mc+/c9Mdp65so69EK4Yp6GjPUhKJ27jDF56aVEd6ZRDTnj430/XeoKkk6veY+52M0VhY3jwqWvo7lUVIClZuLRn9KhuzJm6lZnzNtAx1Id+rV0b9CxcSfIRDh46TKy+LcP6D+HKjmd3u+pxb9WN0fc8i3dBG/qPirT3IpXmJrNz/pu8/+Vs4iLyUT9+H9d0rBEoqbRofftyx/PP4BPfitHhFrbYe0VjWPxFEi3GDcWlZCfr9/ris2QtcWYzrcdfQ+8Qb07HSCk7mbtiO8dTdHQaPpI+HVvhofej05A+dF67lQPrdrJjx24ODO2Of+uzP5vn4a7jsARJdaA0xkPKhAyFdTyHdKGylldUYLHKehgXMpJzIiACItCUBAoLK3/hdnS80K/af75GSoC0+JtX+eCHZeyI9ya06xhuH9KWys4LMymH97B5yRq+e/Mwm4beQbHrI9zVzc9+4+LsBLb8+jHfHenPlCdH2L842SyFpO5awKwtqWz+6gV2xRYzpOeLZ77YcpLl83ZgKbWC30BG9PTHVNULowxH++bD15mf3ZUxdz/DQ2M609JNh+vY/kSuOU6sVo265BibfpvB79ktGHj3I9w4tCMBDoX0692NXtv2sm/jLywKCcJ0Tf+6Z3PTe9G6zwSe6DPhT+NdMeVVrvgjubg42iddqvtL+EVmWJjO0d+X893CQ9iCr2b8PZMZZO9Cqs5Hg4NLCANuuZfBW55kwUfv0Tb8Nf4VpXRZ1bclsGHWao7kFBA0YRwdSnex4Nv5pOdZzr1Q7QWH1/Dz4TX2c2l75rMvU4ezXs2x5QuY7x9E4A0dsMcweafYs2Mra/YlVeajcgBzAod3RJNdkoc5KY44myMBB+YxKzoS/4JS8g+moC71o2jXL7yXU3X78lKKDi/iP7v0DMhL4XeHtnz5YNfKe5xbwsojiVv46oPX+GhxHJn+UdxwdXt74Je6ex6rd63ih0/y0Xq68cxNUUR41NVC7oT2Hc3NT/oz6Ox7mJM4uGszy3YlgKsTpiB/Kv+lVCfU4+IdQf9rR9DuTGdMVU9S38qev+qkZ7+eWsv7ny8mtjCEYQ8/y7Xd3HGuVTxXOo65i6s27eHTVV/zSRs/fG4dTKR3fdOY5HBs1052bjuCtt04OoR7kL7mKxYcyT27BIAXOnLYNuc9tgEl2fEc3DiXeO+xhMXP5p1P2tNt+lUEKsHr0bV8svhgVR5eeHkVse23TSSV26hI2ceBRD86Hp7HwpOR+G0/hu7wAbLNZQSd3MhPXx3ndCyesJGv9uSgjc9kU6IF9SM3MSTcHb8uV9ApeDHerCUm+iCHjqYysHXwRT37V7OCEiTV1GjE+3qNGp1aTQnKzEQN25TAXH0JH3BrWKkklQiIgAiIQH0CNpuNvLw8MjIyyMzMtL8q+2lpaRgMBh5//HE0mr/p+aKSFPYt/Y5vTgdIY5nywgPcNqEb/vaC53Fq1xK+qfgv3yzdyfFN8/jym0gGht9CmBMYXPzofNXjPDnmah64tYf9y6mtNIeji63s2fIlMeVWbNvXsimrRpCUt53N28spU+Yk6tiXbr5GqiewshZmUuTTl5tGDCUyO5oV32/B/jt//iHScsClRTw7F33D3oX7SXLvR+votfyctNZe0qK4IyRbDaTGrGXOh2bMvMTd1/SnzXl7lOprmb/+fIE5G6ulFNxdcTYa6n9up94iFJJxdAOLvprFpuwQrrpnCpOjakVIlTnonXDvNpHnH/iVuEfnMfU1L7xVj3Nf7Wjq3LslrWXG0iPkmMvwSvyd7Yf8SEtMxlxYem7a00cyiVmzmZ0JOoY99SaPjvQlcccRSl1VnJ6GylqCOTuV+Ph4+1UFKYfZ9/tKlNjCvhXFEufdilbBD/DokBCiX36AqRlOuIV0ooU2nvj4MkrMx9jwzUqOmlzo+vI8XtQv5jer+cw9qrKq/WImZuEH/Lg+jczCUAbc/hgv3TvcHkjEL4HHnv2GpQe2s3jRRob37kRrD786epO8aT/yZvtf7bwtZO1ZwCev7mLZLge8w9rR+8ruKAMIz2xG3AN6c8uL7zLpomZWiGfD+2/wzcYiWg15hOcmdMPDqVaEVHmLoMHcNWUcxw5+zm9ffoyrQc8z1/UjzOcCgVLuEXZs287WQznoOp8iLXY32x3ziE/IOlPsc/aKyU06zKaF+7FecT1v/edNOsf/xMw9KkqUtBU2yosyT7dvha2cU+s+5reDoEw2Rnw8yv9MFZN54ul74d37+Fd8Iflu3Qlyyicn3YqZFHbPW8vepEw875zKk9dUUKx2x1hRVjlkzymY0BAf/D0hLiaag4ePkjIomNb1Pgx3TmXsByRIqtul0R31dDLgatLbp/tuSOHUKhVGnRYHnTRxQ7wkjQiIgAj8UwJlZWXk5OTYg6GsrCz7qxIYJScnk5CQQGJiov18SUkJer2eiIgIKuzfKv6eEuceW8v69ZvYGw+OLbty5V131QiQlHu6EtR9NDc+msqJ6J38kJBI0tZlLD1yLQ93c8TBM4iBt7zCwBrFUxuM+F0RRS++JKbG8erd/D3b2Fdutf8MGN6zE74G/ekJG9x73MpzPZSUyhTaOzh6PIU8+4Va2gxR+mzKyc4sRmXUkLJ9FnOzkujZpT0t7L/KexLm14q8pAz2J5ygVWws+Zb+1bdtFK952alYSotwczRh1CkPjJwOG/5A+crIS9rDsllfM29bPt0nPszdk6MIPk9OOkd3ut34Eo+veoJ/LZzOY+U2VJOG0PeGa+jgWtc8iGZi5i9kq7nAPixx35rlJE+axWN3tMUpZSMrjupp1zWSMF9nan37KN7GZw+kcjw1B/OpZHJ1vRn1wIDaD/l7RjDweuUPLNmn2PLLdEoz47B6azl1MAmruzuhPo54+LehhZcXRZ3c8Cv2ISj4Gp56/wHa2wrIivmOO75ZzSldC8YMGsaQ3sPsz6Cdp/pVh0+x6tft5OcUgWd/br2yKy2relpa9RtP/3br2XU0m/i9OzlwPJGozn741xGL1HkPSwZH9m9nw+ajYGxNm/b9GdvnoiKhOrOFfKIXTuO1zzbg1Gksj750I93dTHUEb5WXBw2+m8mT0kn58Gd++HgaJJ5g0qQxdG3XEvdznlEqIW3fTnbui64cYnsslsNpcMO/XuJ+w0kOHT5GiiGCQT0Ca0+rb03j8KrpPLToMDFlejJiTuF2/UO8elVVFbQOOHW7hfe73YISIOXsm89Le+fR78pQji/fSqpnOOHqo3gEtsHXpSVOkU4ExLdBlTuAW5/9P8ZE+uNYuIp/7z7I0aRyerXvwdA7hhJZ6wePAMI6BBEQbIJdMZw8foL/Z+88wKOq0j/8Tk3vvZMQEnqXXkREEXtvq2tde1l1111dy65d929va1nLYu+KCiIiSpHeQgktCem9TsnU/3NuEphAAgEFEvyuz31uO/eU9wxmfvOVU1gD6S2/7nTCsvPb7T7DnReTJ0eagFr3KCLQTGWjjeYuuNCp1N9KWEkK8CM9c9K+EBACQqCFgBI2SugoQaTEUNuuLERKCClBVFxcrFmQVDnlUhcVFUVsbCyDBw8mNTWVlJQUBg0adOisSNjZuW4l6zZuROU86z04m6GjB7dakHxnMoyEtP6MnJzE/2YWU1WZy+LFW7hi+LAO1u7x4LRVsWGuyiynXBwM6LIGM8DHtWjH4u/Y4VIiKYZpo/sR4LfXNzcgg2m338M0327sOi/nuwev476CPIyTL+CWv1zHca2eYzVLXuVfD+xgXekI+o0cz4j2vk67aji4Ew9uVyG/vJ1D+Dkn0z+0I2Gxr5otWJpcmgUtPjKMoAD1k/fBiyRrZS4/f/AC787ZRNQFf+bqq65kamcKCQ9Oq1osdA2OAalkrK9lx4/Pc/3COZxXXclUlV48exp/HJeKqS2feclCXnx/IxHRoZQ3OWlOPoHR/SIIC1Rx/gt48+mN9Lv4Jq4/4xhSdwWQqPS8ZRTW11NVtQ33ihw2FU7lmKEdc2kq28yKr17khY/XUJ1yNldMreCl/IUYxkznlIS1LP30XmY2XkXztmhOOn0UVV9up6AJBpicOPK3k4MJg34ofTpKDtJRk00FbC9wYFfegv16kRwcsDsJRVh/Bg+IIPon2FmWR1FJDbV2uiySmoo2sm7ZT6yrAP9eyfQeeQx9fcJzOupOV+4VLXqJx//+PCXjL+Tqm/7JH0aEEtTRPxmtMgv5S9ZRE2Qitl8fkpb+xMxHV7EhL5eTJvQnIX0g44cPpF9Ca1Rh/XYWLFjBxkI7MTHBVJqySM0aSr8EcOfns/ybN/i4bCR+91/LcSk+62vZrFiKd7KluYH6zb/w0/KTmH7e3qPxuBwULHydmS8/xYtVZ/Ptw5k8Mm8ZFenTuWLGUGa/eT+fZ0bS/K2L0WdMYcuLlZTX2LG7IahoJ1ttVhrJIjkxjOAOvI5j4lMJi1DxU/mUV5VTVdMMCQdnShKRtPf8dcs7yjIUGxKASuBQWm/FtY8MDmoR2ZhgfxJC92FK7ZajlE4JASEgBI4OAspdzmq1UlNTs2tXoqiiokJzN1EuRaWlpTQ0NKAsSSEhIURGRpKcnMzo0aM1QaREUVpamiaM1PPDs1VTWVpLdZlqLYjoyFjiottSMrTvQVh4NKnpA4BirA2N5OfuoJYWkeS01LBjxVf8kq/ecWNv2M53T73GMpMf/Y6/lAkXXclxPnHgFaU78HpVDG0v4mKMdOpJaM1n6Y+r2VHZQPsImAZyVu+k0gaG3KV890kghVEt/bVsX8KmYivaYknth/Arrzy4HPksevNZ/nn9HBKr9fz9T9PpH6Y7gJWoSijaZsXSEEZCVDhBAZ1+091vX60V21j8yfO8M2shtv6nM7VPNLZ1X/DWus5edWOv38acpx/hy5JAsk+/nktGl7B66Xt8cPcTVB4H8yMDmDHyfBICjOgoZeVrrzJffwLnTMvh5feWoH1MWquPGDSR0eYP+eyd2YwYmE7MsPhdYsNeXkJxQy31Idmc9YezOHXqAPZe4slBTcFGfnz3ad6fu5jSzKu4+PRh9M17D0zBxA85kcv/eDaDXnmW+bmbaRx1DRdPK+P1j38mT3no9XLjqionX28kLDab3ns30DGIgh1scjhaXMJio4gwm3wSG4QTEemnpWqHeqw2O86uJRlW5jC2rljI3HnLqSCUPumDmDipbwfJ1p001W7lp4/fwtFmFTEHEpQ9kXNa4/zaOq7+jZT88j+eve9uViSczPTJk0ipWsAn77eV6OioUus/zyeLt6MbcTbHnTEU+/Y5LPzmfd5fH0Jdn9P5yw3R9E7IxEw9OxfO4ed1LqKGTGFM+RK+2ra7zoC4NLLTItDN+pz3P5/AkJuOofWfGW6bheqifIpMYWQMOYlr/nYKfXa/qmWfczuqyfnmI9545mZmmy/h0jv/xPHxP/KYig8J68Npf76dY0L9mL9iLgvSLubu0wJ4/51XKSq0YR8O1FZR4mjGGp5GYmwwwR1on5DYBGLDIgkkn9KqaspqVLDawf0yIiKp3QR274uIQD/So0PweL1UNtn3SsqgPmNmo4HYEH/So0MJDeiqPbh7j1t6JwSEgBDozgRcLheNjY2ahUhZidSuxJESQQUFBdquYomUIFLWpLCwMCIiIsjIyNDEkBJCvXr10gRRYmIi/mrxnCO2NdJQbaFRmZGIIDgoirCD0Ge26nx+eP4yrv+4/UBMAWZ6jZjG+aN8zRtNNNZ7W+ISUL8O6+g0nLboR1647198stXIwFH96BUT3OrWZaN0WwUqd4Bu50ZW/OSksPVX5uaqbRRU2YjIDCYqfF+ruLhoqsxn07JlbG3x52vf+b2uXDisa/jfDd/iPTeLzU/cymOxn/H0H/oRYeiiULIVUbDTSpOlD0P69SI2qoNvfXu1u+cNJ3VFW1j4wXO88/UcLENv5k/ThmL94U2+W67KNtNYsZ3V36+hLDCclKGTGdurBY6ruYkyL3h1JuwhE7jjpRMpflPHUwumcPvtiJF9kQAAIABJREFU8NSsSJyGlmzplm2f8dj7lUy5635OafwXM/fUcxGDGD4wnPde+5IP5o+iX6+pDGyNtK8o20lDXTXBWVPo3783SXv9huugtmAl3776PO/P3UTYjL/w0OWXMcyziI/n7KDB34+MqDh6p2djvOgKSl7bwJh/nE1a8cfE+29neU4F7hQ35aUFYDKiH9qHrhqSqKpgp8vJviKqWoiXUVvfiFULsNlzDva+dlZuYM3SRSzMBUIyyBx6PNM6TFNuo3rnAl6+bQEvt1UTEE7UtFvRXXYa488cRjxqEdZ6cud+xZtPXMbnngu47fHHSf3ufj6eb6dZC1cvZ+2Xi9naZCOo/2TG940ntDWwr6qmSROBbl0fZtxyE8MbBvDRrCbSR8RR5AwnNcKs2S+9lauZ9f1qamNGcPYEAyvfX9LWo5ZjQDwJGVmk6j7h5y8/4+vpvbm0NcDPam2ieOc2jKERJI89lmFt6kl704OruZL137zG/931AtsGXMKd9/6Xywc3suWdp6j0ePAmxhGjj6PvrXew9o//5bZ/nsf4vgXkxJQzf0sRjZY+VFUU09xsh6wUEsOCO7Baq4x+4cQEBmmZGkvKqyirrMFBR3Fk7YfW0ZWIpI6odNN7ulZrktGgJ7jWQo21mWanRxNNBr0Of6OByCA/EsODtGM3HYZ0SwgIASHQYwk0Nzdrgqiurg61t7nOKXe5vLw8zWVOCaKmpibNJS48PFwTRP3799dEUHp6+i5BFBcXh9F4dP4ZNgZG0HvsuZyrpfZVxiQHtuI1zFpawJzH/0hR+f/x7F8v4ViV6YFyyorBoyVjTSQ2xqA88jrcqresZUdjHdasy7npoVs555iU1sxVre52zxdinHY5d/m42zVtmcfHn3zMRv+JHNsvscN6W266sNYUsGbe58wt2kexdo+MxF/wV+54czol9/2Zt7fuwOLJJqJTU1i7l6lZu4yNddU0xE8hIymC8D30sa25GYdmuthTkfjW46ShLJftBTXETL6Nq/54U4uL3UnTuEoVc1dosSI3f5+DNX4w59z3Go+foPIUerBUbmbmVV+ystrEiGPHEm/yJ/7qmcy8uqX+t4e1tVPOL899Tfm0G7l7Rh9CvupogiLJ7JdGaNinfPK/OcwYNYDMCSqzWB35W/KoKnOTdeIA+vdO7ODLrRJ6+ZTVBTH0ovs577LTGBRUweovfuSLr5ZQFTCBQGsT1TX5bFi3jh3ho7k20h9rbQqxKU18O/tHCo/tz/JFi9AbI4nOzmhd9LSt/4f7aKF40zpWLMmhHH8is/sx9Phh7WPD/EKJzR7Puef6uKx5mrFUbGHJz5up/vJRLlucw98yXuDuITF4PQ1sX/gVeVHnctudM7lqmAHjMa8zo21ollncsiSHwiY/Rl3+AM9eOYZ0LeDIxo8PTOeWF8sxDRtAr5hIskfezD/UgmHttgY2/bCUXHsaI848hfFhP7Oy3XN1EUBUTBK9Mj28unAu779zHKfefzwRSojXFZG7roywyImMmzJsl4WppQovLkcdectyYNQN/PPxuzkxzou3cTWvP/w/NoeGE95kweptom7Rlyw2jeDU2AACjGmkDzDzzKJF5F3Ql4KVy6ivqyV0SBKxwYEdJznx88ffZG6Jz6pvpKmxSUv00mak22tI+7hxdP7feR8D7umPlFCKCvJHWZVUWvAGm0NzvVMudqH+JkL8TagysgkBISAEhMCvI2Cz2bT4IJVlrm1X7nLKOtQmiNS1KqcSKigLkXKZGzFihGYlUoJI7cpSpO73jP83m/ELMOGnvrfZ9s1PWdCa7a2FjEb0gbtjOQKj0znhtg85oa0KRxM1C1/krBsfZcGmWja8eRs3RQ3hl0fGdfCFue2lPY81/PLDYqora4k7pjdJYaFdSu0bnDWVy/4+kbrCLeRu+JzZealk9h1AZvSeVht/YrOncvWTU2nVCHt2YJ/Xwx78kJP3WWKPh/ZifvjoM1budJNywbGMyIzfa52ioo1L+WFWDLZegQTW5rCzur2TYUuNgaSOPItbRp61RwOtl1qsSCFbCSY0ZAhjh7etQuvG7aqkvAQMBj1J8T7+j3vVVEZR/THcdudJ9AkLoHKv5y03ModOIClyIaZlC1m4/nQmDE0my7SFjWvLKa7LZtLA/mQmdRBIQhDJAydx8qXRbC6xsH3+52y3FbH2+59YWe2HOclO05KveN3gYsHrH5Gb5WLKlqFMD0mgb3oQljmfM2erjnUb9ZhN0Uwc2n4Nrk6623I7MIgwvV5LFLLv/MHB+Pv7KUPV/jdrHmuXLmb+knLwS2fgoPGcOs7XeqqsS8kMOONvfHiGT3WuRirXfMg9f7qf/6wuoskyj6de+IVbXzmNIFMqpzz4IW15EHzeajlVboNOJ3YGMXZECqHBbcK6muoKB8oAkxgZQUCnlup6aq2xjB4zhmPGZRK84ee9mlA3IuPTyOo/kuBPVlG05Cd+KpvA6TF11JasZ+WaMCInjmFKezOSWpgLv4B0plx+LfqNtdiWfM7najXn0q+ZtdmLOzOKaJWV8ssGtjz9GN80zmDwmuHcODmL7EGpBHy+gMUbB6Bfb6GxKZShfdKICuvoc6QsSWFEBAVyEAbwvcbblane6yW5ceQJqBilsACzth/53kgPhIAQEAI9l0Bb/JByh1O7EkTqqLLLKTG0Y8cOLbGCSrDgcDgIDAwkNDSU6OhoJk2apAki5TrXu3dvLabo8MUPHQrm0cQkRxClDC7b7VgsDTQ0OSB6b/ftpsY6SkvytE6YggKJ6pW8x6/HPv0zBxM+9mpevHUh46/5ijq3m81fL2DbI+MY7FNsX6e12z/jux/LqKiBDF0Nm1YsxKplglBv1bF6Sxl1zWAoyGHpD19ja/edv4mtP3zIBzM/ZUvsGVzz94f5+0X9fpMvUvvqc+fP7JT+8j8+mlOLIflCbr9kGoN6tUX0m4nuM5Jx05xE1TWw4ft3USvLxAZ62FK+H+W6V4NubLVl7FibQ3FAKCOyRpDdppH2KruvG0O47M0h+yrQ8iwxhaygYMJZzdyFazl38giygkspbGzAnj2cgf0y6FAjAY6q7az8/n3eW1yuKfTaonxy1xbhjEmlb1IE7tqf+e8Ty8mtDyEiYiML15Zz1smRDJ58HMHvvc/Hz3vJz/PDv/dETh4bt/++tpWITSDDaEKFblktNuxuj5ZlscVWZsduc+PScmn0JjGuK+6nTupzc1i9ejWb1JKwqZn0HzuOvm2L3ba129HRGEJM/1O58y9LeP+i16l3u/EUlaGI7Nt90Erh0uXk2u34Z45iQHIQBx7elsK4yy9nXGu/NI/bjvoYEkZUXCK9+YYdRYuYs6CU02d4aSouIj88muQxk9mlw33e97jsVK7+nNf+p/7RevF6Ksj5dgU7iaBffG96BVfywT9uZ9VOB864TfywrIDLR6XTf/JpZDz7ID+//R71SyqoNgzlD8MziYvc80eO1sYcysvKRVdDx3y6uNepiKS9kMgNISAEhIAQOFoJuN1uzRVOxRApIaSOShSpRArbt2/XBJHKMKcEkYofUhnmlOiJj49n5MiRZGZmamJICSJ178jGDx2KWQolITmJuMRQ2F7Ftm0bWLuxlGm90tqnF3ZbqCrZwabNyi/Nn+iIVMYM2pcrG+iNJhL6DiKer6hTKczLamhZmjKAwCBocYKwYLO1xift4RRRumIdtqy+jIzpC+U/88VbP0NDIWtz8ynXRZCgt2C1gm7tfD6t38aPPl5MOOspydvB9go//I02GsrKqObIiaSKzfN447Ov2Bk8nMvuv5aTh6T5WJFUWu67GX7BHvOb+yn33P8gvyza4/6+Lt2NlOxYwZdfriQ0eTyTrphB/13lPXg9NqwNenS6UIJ8ee0qc4AnUdHE+vlpqaGrGq3YHU4aC7aSV9NE7LCB9MvoyNWupY2g3pO4+K5JXKzy+zVtYu6rj3L//YsIPfMOHnjwUhK2fMi/b7uKwsphTL/uXp48t5+SNYQPncw0/5d56f330QdEkTFxBuMOJE4/IIKoMAMmpYq2FlCkhJKWtkSFc+WxbVsDdSpGLSiZ+NgwQvaKp9qDka2Y5Yt/ZN4iJZGCyOw/lGnH72cxW98qDAaMoWGau2C9TocuaLeF1rdYu3PbJt6dOZeGBiOj7zyTUTHhPum5m7E3KathMP5+JowdeUq2q2w/F4GBBIVHaIk3Njqc1DRacDQ6KNmeS11UEudMHt666HT7egz+oWSd/xSzzlcayUnD9pe4sM8KGrPP5v43X+Ss2DLeunIIt5X4k3rJw7x14xSSwv0gfDKn9HqOh775nBIHBE+8iuGZsXSmkaivo8bSRINq3mzGZPJNxNG+T/u7EpG0P0LyXAgIASEgBHokAZU1TokgFR/UtquECsoytG3btl2CSGWdU7FBShApC5HKKjdlyhRNEClRpHaVivtojR/ac3KT+45m0KARRC2fT9W2DSycv4DjBp3GiJTwlqxfbis12xfx/RdfMnslGIPS6Df0LM6coEwULuwNZeTllKj84fSLaw2y8bpx1hew+vsV2jpJOr2esEnDyNQaTyQpTYdery42sD3fiUstJdPmLdTawf7nP8Mr6gtW29ZYzPqvn+emh9/CkXEGJxvWk7PoZ4yn3cQ/fWKStOIVC3nt4Qe455lcBpx0OhdeMaV9fEhbnYfpuPjlJ9k54GRufvJf/HFyH2I7+VH813bHWV/CjhXfs7gkkugpk5k+wiflm9eJozGfLVuM6KOyST7ItWTa99EP/0ADhrheTBqaTVq0mc1LVlJWG8ywgf3o3ZkZqV0lLmq355ObswXbkDSy0qOxrfiMWT98zOycOAbOOIU/TVcCSW3+BAT1od8xwFdg9vdjxJRxnecysxWzdsV2al1ugtOHMzglFHPsaKaNi+PzbeXU71jPktxijs2KIyjASOPmH1i0rpiyRogYn02vxBhCm8rI3V5ASY2VgJhepKelEOeT7ty2fQVLFv3MwjIwhw0ge8Bk9vb+UxkfKynZWYwrdiBZbR8AjxN7dR6rF6zR0uUbTCYShw3oIAV/6/BbD7WrPuW7HCvW8GM5ZVw6Yb75wJuLKNhhw9KUSmxUMAG/WgwbMJjN+EWGk5I5gCn9Y6mtWsq6NVuITrqaYzsyI7XrrhePy8L69+dR1CeMxKkTCd/+Mz8v+YpXvrZhSDyLO88fQ+Su1HWZZI/wJ3AFUA19Rw0lIzqic1dbhwObcgVWn464KGJiIg/aYiwiqd3EyYUQEAJCQAj0NALK4qMSKighZLFYtF2dq3ihrVu3arsSRsp9TiVaCAgI0ASRiiEaMGAAffr00YRQVlaWZiUKDg5G3/KNvaeh+G36mzKc46dPY/3yDXy4fBXfvvkM3uYGbjhvaIu1w1rA8ln/4+nn5qAyL4ckRJNx7Ah6a603UrrpYx4681mqrvk3/zih1efNbaVh04fc8cB3oNNjSBrIBRdOJqm1x/4BIaBTrmSb2ZrXhHNMNJj2MCX5jM5RvYPc717ihoe/Js8Vx/knjiVrcyk5u8pYqcoroLDBSHRKCjHlxewsLaTMHM34qHhiuuL6tKuurp/YS9azKq8BT2xfxmREYmxbX2iPKqIyTuDGJx/issm9iTlEAgmnhaKcpXzyxXdUZk3h3EvO4zgfjaR8yFzlpRSiQ6cLIGCPpBF7dLmLl0n0m3IM4wcP58oLpzI8toqP8+qwxgxnYP90EjsJI2lfeTNN+jiyRl7E9RkL+M9bf+bszYWoRM7BqYMYPKQvwcXbqAzNJJJ6SrcsYPW2UGJiPDQ5XRgbdpJXF096R3O84z2uP/keljXayfz7PObdOZHEsDhGnTKdvj/kU5H7My8+P5MBnmoGJ4ew+eO3WZiXT0N4Cn848wTGZqfgXf8MD9/1FG//WEDCKXfyyD/u4I+jW30YXY3kbljNunUblRmDxBFDmHDKuA5c5exUbJ7NK/f+m9zxD3D7lNaJcdZTvvoD7nriB/RGM3FZI7jmBJX7eh9bXR4fvPIqq7wRZF11Jaf1jSDM9weGygpKHc3YCMFsNmDQfozYR337fRRKTNogxl1+MUMm3Mw1o81s+ymfvJoYEqdOYrjvZ6zDurx4aKDU/yL+81Aidz73BJe9vIEyj1dFKdH3gknEV+VRZQnGL9SMtfBHlue4MAeEYjZb8LOWUlZrwxrrR6DvOFvbclaUUlRfo/QUfWKjSYw+mJQNLZWJSOpwAuWmEBACQkAIdEcCKn5IJUpQaxCpXYkiZS1SLnJbtmzRdhVHpASREkrKOqRiiFTihDFjxqCEUHZ2tiaMVFIFP79D9Q21O9Lrap/CGHDqH7i80oHH8R7zSrcw+7mbmP3c7veN/v74h4URUF9PY3kJy2d/wXd9jyM5yIzBHE9CYhCLXjibiQ/sfkctVGQwxtN3dDZxZz7G82e3SSToPWQCofovsOJi4458HK7kvU1JqipHDflbC9n89aM8+Okmag3HcdczN3LucRGsevBTn8bKWffhU/zr63qyzzyPE7w/sCpnE+aUk4jNTPJZo8dBU3UZRdtK6VLWb58WOjot+vBaLnp2HY7pDzHn9jMZMT6TCJNhryWaBp5+Cn30VexYU8WOjirq6N7OrRRXWzt6svc9p4W6zfP58oOZvFMyjtOvuZ1HL1ZrWu3ePC4n9RXFWPRmjLFD2Gfiv92v7ecsmok3Ps/E1lLOHfNYnlNNYMYM+vdKRFtxy1bDzqJiSmv0RCcnkZQUvodVIIj0QcNJzcygsqQ/gYYI3nz0G4ozUgjzNrH2+Ss5/cOp3PqfxzjFsoA3n/gXszwn8aer9Hz29pd88rcZeJjNbSf3YWRaW5xXa4eKd7LR49HSXRdW1uD2qFQNJuJOvJnbz9uO692v+eWX57jh590f9rCkbAae9WcuO3kk/aNh2/zt1NVWaBUqV7N66+44MVflKpYtXMoiFURmSiIjYxjD+0Z0wMyAOTiG8AAPq546i4n3+hTRmzBHZjBieD8m3fgKN470ebbnaX0BK9+7h3vmhpE47Q7+++epJEe2NxWpz3djsx1X+HB6JYYR2v7xnjV24TqQ1FHnc9eoFrOup34LRRtWsKkpkSmTh7VkFXQ3Y63ayfr8Bsz+cfQekszu5Pt6jKZUzrk9BXv5QJ5I68sTk/7Jqj69iQo04Jz3N85+M5FLZs7k9v5elr5wA8+vz+D0s0eR8MuPLH3jzzzkhbo/nsVJg2OJ2EMpVVYUU19bBfgRGx1NdMTBp3AQkdSFj4MUEQJCQAgIgcNPQGVPs9vtmhhqE0YqfkiJoM2bN2sWovz8fE0QqbJtFiKVWvvEE0/UxJASREoYpaSk9JDscoefc8ctpjD+ivsYf8I0Zr7xCk99vr5dsfCUFFITYyifM4tFO/JY+/6dnPp+GBkjLuThz1/i/m+HMvrJi3lknu9r/gSFn80T825jtO9tIGnydI4xfsO3uNi0JR+LcwxeTD6Lsloozy2gIm8WD937ARsaHPS55F88+8fpDE9W3/pUaHvL5nS5cLtSmXb+eWyoeYUXnr6O98sqcQbFM+y0sUwYnsXur6115C/7jBf+8TbL2ir4VUc9AwcPhZKP+PvtNh767i9MiQreK1Xx2o8e494fgvA7kPgQex0lpcqHKxm3w6UlF+ioq57mRqo3zOWdN17ivvlmzrnmLzz2l+OIVa5cDRXk7SjXEhfaawqZ+9/XyQ2MIev8ExjVUWX7u2crZ/vG9ayxFrePWWt9r3rRXJYWOAjI9NJQnsuqJiBvLs+89F/enmdixjW38Nd7z2N0YthuoeRoorqihE1Lv2PWB6/w1qowJl14J/f+62bGuDcw/827uf2Tzbz00D3kFn7NvOAJTLj1r9z/hxTOSvFw0QNf8d6fj+XLhU/w0x1j0Omi6T0sRfuS3txs06wVEMiYAen47UpVF8+J931AfPIf+Nf7m8j3yVow7pqnufHsiWS3rvuTOXQax0wspsiQT+LI/mQl7DZZWasbsBr8SRg+nITo4YyZOJIONRL+xPc/meuejiPpmWt4eoEPaL9I4ifezKuPncq+ovyaK7ey+dP7Of7RlaQc+xfefPYPDI4KxKh+SNhWTJ3dicqqv/7t/7K4qBD9tDEMTo7jgO0qziaqi7ayblWIz7+b3f21l6zlxwVrKTP2I5IiVq0qgqYytn77JBc8uoyYlAt57Jv7OGdgYovbm9eDp76QNdvKKfzgT1z6gYuM/qdxz3sqRimA6i9u5fInvubTd15BX/oVS2zhRJ11FdffdjIxG57l1ode46s3/8zF6zby5E1nMzmrF3HpicRGBGLCRnF+HtUV1WDuR2J8CnEHPODdYxORtJuFnAkBISAEhMARIKDc5ZTIUUJIiSJ1VHtVVZUmhNoEkRJHyoVObUoQKbc4FT80YcIE+vbtq+1KEKmsc7L9RgSSx/GHe9TeUX1V5P4wlKdvfYF5TTWU5e1eZTMovi/nPL6Sczp6raN7yf0ZHKVnbhM4N21kq8VBSmQAfjoLFVuLqLYv4z8X/5t5lmY8gy/l4f9ezgkDE+joR/HKmlrqGptg6PHc9NhIJo16kf/8532qhlzJ1dffwrR22aFjGXjSLbx00i0d9eo3vufEYfPi8fhzwt9n8sjFI8nYw9CxzwZbEzc8+H4dDWU1mvuZbw43p7WW6vIiCjYs5Zt3n+WlvH5cdPe9vNRmQXLWU7rqPe699Q1y7PWUbi2mwWAitm9frjrxoCQSlM/l5buW85Z5H2pPPfrxNR748TWf4fkxaBAULp7H159mknbjJOLqq6gsq6B+50o+++AtnppTzKBxJ3LXG/dx08RIHNZaqoo9JB9/Iw+a3+WfDy5h66TreO6lBzilNZ5qyB9f5AOdlwsfmcvmD/7KqasyiQk4m+dW/YMJOgebc3LwuN0QPpUzJqYT5N/+a/CQK2fyyZU+3ezotM+p/OO5U/lHB89CB57KrU+fyq0dPOvoVljaSC55ciWXdPSwo3teL40lORTUQuF7V3HpO/UkXvw08+46jpjg1gyU1Yt45W9PMy8/n53bi6myNmOMSuGCycfQK3b3zwMdVd/hvcZclnz0BAXf7dvqHsYGZv71Mmb6VDJokEp7vp63bv+Qfl//iYyC7ZQ32LH99ChjHljFwMRQTntoFq9flIbZ4KQmfyN1fa7k3/dFcvt177DQbzR/euUZ/jg6mUg1vMS7eBowPvYWny+eyYN3fMfL4VO57ombueikgcTac1izTCXhURppMH0HZpO+24Tl07Ounbb/dHTtHSnVjQgo/82WZcqVT3E36ph0RQgIASHQAQEliFQabRVDpASROiqXOeUel5uby6ZNm3ZZiFSSBZUsQWWQU4uyqtih0047TRNDanFWlWFOCSXZjhSBaLKPu4GX1l3I9kUf8u+rXmdbr3h8YtgPoGPjOOmyBF59bAf2DbOZt/FmxsSF4ue3iY/v+BsztxRTy1T+8tFfOWt4qk8muLYmDARFJZGW2ZeQpGhC/dsCL8IZcvZdvHj2XW0Fj+CxDpsjhdS0/gzvE0HwgcYAmUOITkynb18didF7W6dqcufzv2fu5r9LITx7PH99+BHuaIt1UaP2iyb9uDv4aN11VG6fxSOn3M+8sDgm3PEOt+9p2tsPJVNYEhmZWRiH/4WP7jyVgYkH79LU0lQDGxe9y3+eeoXvVMLE9PFc88zLPHpWdmtPbFRumcfr99zDe9taMs1l3/B/vPTAjHZJDQyBkQy54nUWpz3K5H+Ucufnr3ORSgKiNucmZs9ai9MZTOKN13NuVjBBPexbsNfjZvUr53H9x+D2wHF3f8Zrl2YT4tf2eQcSTuXhL08FVvHq+f/g7XV5hF/0OLedOYI+B2BVMQSEEZvcm74nDuCSa27hrpNaog5baR7gwYW1dj1v//0inlFGaaOZgTf/j3X3TvCpp5y5D1/OE0sasKiU68Nu4LXnL2NUQmi7HC6x0+7i7fhkrvnXz8Secy1XnTqCtNaMg805K1lamMc2TPQd1J9B2Wk+bn4+TXXxVOdVf7Fk61EE1Iy5PR4cbg9Ot1tbTNag0+Nn1GMy6jHo9T4uCj1qaNLZw0jgpptu0tZxuO+++5g2bdphbFma+r0QUPFDShC1iSIliFT8kFqMdePGjZogUnFE6lrFD6lUrcpCFBMTo7nKKSGk9n79+mkWI4kfOso/OYVvce7oG/mqrIk+d8zmq79PIS3CLH/PjvJpP1zDs668n2kTH2Zj6M28teGfTI8K6tBF8HD1R9r5rQk4WPnKn/j7E28xt2gAF9z1IHfdcgaDxJL0W4PuvvV5vF6szS5K663srG2i1tqsGZIMOh3hgWZSI4NJCAsk0GwSy1L3nUbpmRA46gio9YdUyu02UaTOVWptlV1OCSK1K0FUWFiolTGbzVpChaSkJC1+SGWZaxNEav0hg2Ef7jtHHT0ZkEYgZQpnnpDOxoX1NGzcSEnzOJIwt/sVWUgJgYMlsPyzmVQmJHDFc/cyNVQE0sFy7L7vbSd/mxu3I5XUGaczdcqIXyWQ1DjFktR9Z3uvnimjX63VweayWorrOs5wo9fpiA8NYGBSJKH+ZhFKe1GUG20ExJLURkKOB0pAxQ+pXQkhtatzlV1Oucpt2LBBE0QqjkjFDynxpCxEav2hjIwMBg4cqKXdVkcVPxQRESEJFQ50AqS8EBACQkAIHHICPcwb85Dz6NYNNNid5JbVdSqQVOeVpamswYaOGoakRBPkJ1PcrSdVOicEujEB9cOMEjltokidK0uRWnMoJyeH9evX7xJEdXV12khUDFGbu9yZZ57JoEGDtF2l21bpuGUTAkJACAgBIdATCMg36J4wSyo3A2gudhVNu/Pxd9Z1JZRqbQ6K65rIitudmrKz8nJfCAgBIdAmiJQQUruKJ1LrECnr0Lp16zRBpKxEKrmCuq8WW1UucSq19tixYxk8eLC2K1GUmJiIcqdH4ojRAAAgAElEQVSTTQgIASEgBIRATyUgIqmHzFyT3UmtpRmHS2W93/9mc7ioaLSTHu3B9OuXV95/g1JCCAiBHkNACSIlgtp2JYpqa2s1MbR27VpNEClhtH37dk0wKUGkXObassu1CSJ1VIu0queyCQEhIASEgBA4mgiISOohs1lnbaax2dnl3irLk8PlptHuJDJo37ntu1ypFBQCQuCgCbQJE51Od1hFhWrXd1cxRCrd9po1a1CCSB2V25xKqKA2JXiUW5yKGbriiisYMmQIQ4cO1eKIVOY51X/ZhIAQEAJCQAgc7QREJPWQGVZZ7BrtjgPqrbI5uSXD+wExk8JC4FAR2LZtG3/7298444wzOP3007VEBoeqrbZ6lVucalcJodWrV2tH5TJXWVmpFVGCSC28qkTQeeedx7Bhw7RzZTGS7HJtFOUoBISAEBACv0cCIpJ6yKzHhQZSbWmmqmn3iub767pKC24SN5j9YZLnQuCQE1i8eDFXX321ZsG55JJLNNe137JRZSlSiRNUmm1lHWqzEKn022pdIvVcucslJyczadIkzTqkLEQqfkjdU9ahPfffsn9SlxAQAkJACAiBnkZARFIPmTG1BlKIv6nLIkmlAg80GwkNMPWQEUo3hcDRSeCTTz7htttu0+J/3nrrLY4//nhtwdSDHa2KHyotLdVSbau4ISWI1HHnzp2oxVpVnJFyi+vduzennXbaroQKakHWtvghZUFq28V97mBnQt4TAkJACAiBo5mAiKQeMrt+RoMWW1TRaMPS7Npvr4P9jNqiskosySYEhMCRIfDUU0/xyCOPEBcXx8svv8yIESPw9/fvcmeU6CkoKNiVSKEt5XZ5ebm2PpESRGFhYWRnZzNx4kTNMqSSKaj1iIKDgzWXOeU2pwSROoog6jJ6KSgEhIAQEAK/cwIiknrQByAxLEhLxJBX1YjT3XmWOyWoEsMDSQwL7EGjk64KgaOHgEqOoOKP3njjDc217dlnn6Vv3777dLNTbnEqfkgJIbWrdYi2bNmiZZ1T6xSpLTY2VhNEykLUtiircpdTwkutT6R2JYZEEB09nyUZiRAQAkJACBwZAiKSjgz3g2rVz2QgKy4MZR3aUdXQYTrwAJOB9OgQ+sSGYZTU3wfFWV4SAr+GQENDA9dccw1ff/01p5xyCg8++CBpaWmacGmrVyVOUOsPqSQKaleCKD8/H4vFolmIlMhJSkrSLE/9+/fXMsspkaVEklp/SIkhFWOkdlVWNiEgBISAEBACQuC3JSAi6bfleUhrU45z/iYjfePDNUtRWZ2VKosdp9uL2aAnKthfux/sZ5K1kQ7pTEjlQmBvAio5QnFxMZdddhm//PIL119/PbfccgsOh4PvvvtOS6rQJorKysqw2+3aMxU/pETUscceS5sgUvFEyo1OCSK1iyDam7fcEQJCQAgIASFwKAmISDqUdA9B3UooqcVhIwP9CPU309vjRf2nQ4dBr8Oob8lSdQialiqFgBDohICKDVq1ahVXXnmltgCrSqWtEiqcfPLJWtY5FVuk9oiICHr16sWMGTM0QaREUUpKirYukZ+fnyaI1FGJIhVHJJsQEAJCQAgIASFwZAjovOrnT9mEQA8kUFFRwauvvsrMmTN7YO+PfJdV8L+yZqh1ctTiobIdGAFl6XnyyScZO3aslllOpdZWliQlmBTP1NRU0tPTtRgilVmuzV1OWY5UDJHa2wSRJFQ4MPZSWggIASEgBITAoSYglqRDTVjqP2QEVKC7cmtSrkvTp08/ZO3sr+Jgo5swkwuLy0Cds+f8k6qtrUUlBAgNDdW+rO9vnPK8hUBbCm6Vdlst0jpu3DjCw8M1sanScKtNic+EhAROOOEETjrpJC27XWBgoBZLJByFgBAQAkJACAiB7k+g53yj6/4spYeHmYD6xV59yc/KyuLxxx8/zK3vbk6XtxBdwSK8qaPxpk8CXc9wk1LslCFZBf6La9fu+dzfmRJJKvvcxRdfrLnQqfIhISG899571NfXs3z5cubOnau536kU4MrSOX78eKZNm6YdlSgVy9H+KMtzISAEhIAQEAJHloCIpCPLX1r/lQTUl03lvqTiOo7E5q0vweUqwt1chMGZjCHIgz4q7Uh0Rdo8TASUSKqurm7XmhKZSqwr0amOyrKp3EEXL17M/Pnz+eyzz5g1a5ZmXVKWp6lTpzJ69GhNXIlgaodSLoSAEBACQkAIdAsCIpK6xTRIJ3okAa8bT8FSPMVrwWnDXbgKXVRv9GGJYPTrkUOSTv86AkrwqFgltasEDUowqTTgJSUlmmvoggUL+PDDD/niiy+0pA633norap0j2YSAEBACQkAICIHuRUBEUveaD+lNDyLgqdimCSOvpaal1/YGPIUr8MRmoU8a0oNGIl09FASUdSkyMlLbVUpvtfirWgS2sLCQZcuWaYkdlBVUNiEgBISAEBACQqD7ERCR1P3mRHrUEwi47Hjyl+Ap3wRed0uPdTo8Vds0oaSLSkfnH9oTRiJ9PAwEVNyXyiKodmVdUinC1YKwKj5JNiEgBISAEBACQqD7ERCR1P3mRHrUAwh4itbgKVqFzi8IDCa81hp0kb20nnt2LkcXnYkhc3IPGIl08XATUOIoNjb2cDcr7QkBISAEhIAQEAIHQKBnpOE6gAFJUSFwqAkoQeTO/wVPXRH6lJHo4/pqTeoTB2JIHoq3qaIlVqm28FB3ReoXAkJACAgBISAEhIAQOAQERCQdAqhS5dFNwFOwDE/pevShCRhSRkBwjDZgXUA4+rTRmhXJU7Jec7vD7Ty6YcjohIAQEAJCQAgIASFwFBIQkXQUTqoM6dAR8NTk4y5YCrY69L3GoIvvj05vaG1Qhy4mC33qKC1OyVO4Ek/l1kPXGalZCAgBISAEhIAQEAJC4JAQkJikQ4JVKj0qCbidePIW463IRRebjT5lOLqAMPSZUzBFZaCPzkRnDkSfPAxP+Wa8Jes0tztdeAo6/5CjEokMSggIASEgBISAEBACRyMBsSQdjbMqYzokBDyVW/AUrQavF0Ovsegj0wEd+tgsDNnTUBnt1KYPT8bQazT4h2prKHnLNx6S/kilQkAICAEhIASEgBAQAoeGgIikQ8NVaj0KCXgby/E2N6JPHo4+cTCYAzsepdEPfcIgDCnD8TY34G0o7bic3BUCQkAICAEhIASEgBDolgTE3a5bTot0qjsS0MdkYRx2PrqwJHRhCfvsoi44Bn3fEzS3PF1k2j7LykMhIASEgBAQAkJACAiB7kVARFL3mg/pTTcmoFNudOHJXeuh3qDFKBGd2bXyUkoICAEhIASEgBAQAkKg2xAQd7tuMxXSESEgBISAEBACQkAICAEhIAS6AwGxJHWHWZA+9GgCnp3L8ZRt1LLaabFKPXo00nkhIASEgBAQAkJACAgBsSTJZ0AI/EoCnqJVuFa+i6c051fWJK8LASEgBISAEBACQkAIdAcCYknqDrPQ2ge3x0uzy43b40Gv0+Nn0mPUd65jXR4PDpcH9Z5Br8PPaNCOnQ3Jpep3uvB40cr5mwzodbrOist9ISAEhIAQEAJCQAgIASHwuyQgIqkbTLvT7aHO5qDW0kxTs7NVJOkI8jMRHmgmItBPE0BtXVVCqs7qoM6qyrtwe70Y9TqC/UxEBJoJCzBjNhq04l7A4XJTY2nW3rE4nHi08npC/FV5P62NfYmxtnblKASEgBAQAkJACAgBISAEfg8ERCQd4VlWgqeo1kJxnUUTMkowtW1K+CjBkxwRRFJ4kCaarA6XVla9U29z4FveZNBrIkmVTY4Ixs9kwNrsZGethZI6iyaSlEBq21T5qCA/re7UqOB9Wq3a3pGjEBACQkAICAEhIASEgBA42gmISDqCM+xyeyisaWJLRT2WZtdePVHucdWWZmxO5YLnJS40kMpGGzuqGjWL054vKMFU0WjH6nCjtFBMaADFtU1sr2zU3Pg6Kl/eYKPR7kSn09ErKlg77llOroWAEBACQkAICAEhIASEwO+JgIikIzjbNdZmTfB0JJB8u6WsR/nVTajyTXZXhwLJt7xy2dtR1aC58JU1WDsUSG3llV1J1b+5rE6zKoUGmNseyVEICAEhIASEgBAQAkJACPwuCYhIOkLT7vV6Ka61aAKlK12oszVTY7Fj0Ov3mZyhrS4V46TikPR63X6TMyihZHe62FnTxMCkyLYq5CgEhIAQEAJCQAgIASEgBH6XBDpPnfa7xHH4Bq2y0ikR4xtTtK/W7U63ZhlSx65sNoeLiiY7LvfuGKR9vaeSP5TUW/dVRJ51QkAX3QdDn+PQR/bqpITcFgJCQAgIASEgBISAEOhJBMSSdIRmy65SffskUdhfN1SCByWQAkxdmzJVtyqv0oSb2b8WVl1R5ZWFS8UnydZ1Aoas41C7bEJACAgBISAEhIAQEAJHB4H9f3s+OsbZ7UZxoDpECZcDES86dJpb3oG0oxdt1O0+J9IhISAEhIAQEAJCQAgIgcNPQETS4WeutagsQgeymGugyUCwnxGjoWtTptJ7h/gZUceubGpRWZVu/ECEWFfqlTJCQAgIASEgBISAEBACQqCnEeia71ZPG1UP6K8SL/GhATTYHFqK7/11OdBs0hZ/9XjR0oHvr7wSVGohWhX75Ls2UmfvqTWZ0iJDOnss94WAEBACQkAICAEhIASEwO+GQNfMDL8bHId3oAlhQUQE+e03W52y8sQE+5MVG05sSMB+yyvBExcaQL/4cE0o7c/lzqDTERnkT0J4wOEFIK0JASEgBISAEBACQkAICIFuSEAsSUdwUkL8TWTFhuHxQLVFZaLzsGcuOqNerwmdzNhQbTHZ8garlvBBZcZT5X03FVKk3PGig/3pHRNKdHAAgWYjG0trtQVj1eK0vltb+chAPwYnR2IyGHwfy3kXCXgbyvBaqtCFxKELjuniW1JMCAgBISAEhIAQEAJCoLsSEJF0hGcmJiRAW8uooLpJE0pt7nEqNsisBFKQmfSoEKKC/bX1jpT1SYmZgupGbXFZlUJcudMpa5PZoCcyyI+0qBBNKKmhxYUGasJpR1Uj9VaHlnLcS0t5P6NBW0BWCSpZRPbgPwjunC9wrf0U46g/Yhxx0cFXJG8KASEgBISAEBACQkAIdAsCIpK6wTREBfmjrDkNdid11maaXR6Uy5wSPCH+5nbudcp1LibEXxM3DXYHtVaHluZbxTiFB5g1saMEU9tm0Os0Fz3VhiqvLFBulRbcaNBc7EL9TW1F5SgEhIAQEAJCQAgIASEgBISA8s4SCt2DgLIcqexyau/KptfrNAEVYDai1jhSuki55vkKJN96lFiKCPTTdt/7ci4EhIAQEAJCQAgIASEgBIRAewIiktrz6BFXaoFYtfCrsjrVWptxur2aq124EkFBZpQbnRJMsgkBISAEhIAQEAJCQAgIASFw4AREJB04syP6hopBKqmzsK2yQRNJvrkY2qxFKsYoMTxQhNIRnSlpXAgIASEgBISAEBACQqCnEhCR1INmTlmQVMKGrRX1NDW79uq52+OlqslOs8utraWUFhmsJYXYq6DcEAJCQAgIASEgBISAEBACQqBTAuKT1Sma7vegrN5GXlVjhwLJt7eNdifbKxuobLL73pZzISAEhIAQEAJCQAgIASEgBLpAQERSFyB1hyJqTSRlJaq3O7rUnaZmp+aWp5I6yHaICZgC0AWEgdH/EDck1QsBISAEhIAQEAJCQAgcDgLibnc4KP8GbahU3/U2h5bJrivVqdglJaiaHE5C/CTNd1eYHWwZ4zGXonbZhIAQEAJCQAgIASEgBI4OAmJJ6iHzWGOxayLpQLqrYpSane4DeUXKCgEhIASEgBAQAkJACAiB3z0BEUk95CNgNOjaLSrb1W77rCvb1VeknBAQAkJACAgBISAEhIAQ+F0TEJHUQ6Y/ItC/ywvNtg3JpNcTaBKPyjYechQCQkAICAEhIASEgBAQAl0hICKpK5S6QZnwQDNhASb0XTQNqQVlI4P8CDCLSOoG0yddEAJCQAgIASEgBISAEOhBBEQk9ZDJUuIoNiSQqCC/LvU4xN9EUkRQl8pKISEgBISAEBACQkAICAEhIAR2ExAzw24W3f4sNiQAlQrc5fFSa23utL/KgtQvIZzwgK4Jqk4rkgddIuBa+gaunC8xjrgI49Bz9/mOt24N7tUv49pRji60P/phf8aUGb3HOza8DT/h/OhlPO2e6NHpe2M483GM4e0etFwUvkbzT/Pw2jteH0s/7J8Y+w1GH9DBu3JLCAgBISAEhIAQEAJCYBcBEUm7UHT/E+VplxAeiNmo1xaLLW+w4XDv/hqtXOwSwgJIjw7VXO266JnX/Qfe3XvodoDDCm7n/nvqdYPbppX3Ou3gab+QlbepAPeS23EXOvE69hY7XnJwfXou7ogLMZ12OnqDYVebnvp8aG4ER8f98Ky8D8e2aRgnnYUhPh7drjflRAgIASEgBISAEBACQsCXgIgkXxo94Fy53UUF+xMe6IdaMLbB5kCtiWQ2Ggj1NxHkZ9Ky4HU1dqkHDPl300Vv4w7cS+7Eld8I7gB0wTMwnn0pLT6xLnAtx/nOM3iaG/BWvI3z50T8Jh4DbULJowRzi+jS9fsrpsGD0PmrNbI24Zr9Ou7yIqiZjafwGPSh8egCfzdoZaBCQAgIASEgBISAEDggAhKTdEC4ukdhJYBMBj3hAWaSI4I1y1FSeBBhAX7afRFI3WOeDqgX7lqomo87vwHcgehCpmI8+0oMgRHotD0GXcixmC6+uUU0eWx4t36Eq87VpovaN2cMRhfQ9u4xGIdPQB8eDt5mPGVbwFLXvrxcCQEhIASEgBAQAkJACOwiICJpF4qed6LTtaydZNC3HMW9rufNYVuPvZZK3Ftmobzx8A9F138GhsDgtsctR10guuDJGNJab7tyce8sAc2C1L5o+ysTOrM/GFr/ubudeL273TTbl5UrISAEhIAQEAJCQAgIARFJ8hkQAkecgAOcFXhrrS09MZjRRbUpoT06p/NHnzEGNEXsxLtjJR63a49Ce18qEYarJcZJFxSBziRJPfamJHeEgBAQAkJACAgBIdBCQESSfBKEwBEn0AyuCrz1qiMmdIYEdKGdhQvq0Jl8golU8of9bZZfcOVuwNOoysagi0lBFygp7vaHTZ4LASEgBISAEBACv18CnX0T+/0SkZELgcNOQCVbcNGS71vlnDOh+5U/X3i3vYSjOACdXgeeRrwNteCJQj/iSoyZ/dCZf2UDh52RNCgEhIAQEAJCQAgIgcNHQETS4WMtLR2lBAyDz0SfeSy6oD3XOzqCA7aV4rX55nSIaRFI/cagDwpA8n8fwbmRpoWAEBACQkAICIFuT0BEUrefIulgdyegC45F7Qe/mcAQgS4IsOyvFi+exrJdqb4JiUXXgdlJ1/sqjNlZ6FyrcS2djae+Hu+2eXhi0tEF9EIn//L3B1qeCwEhIASEgBAQAr9jAuJz8zuefBl6dyHgB8ZYdKGqPy68rhI8ldUdd87rxVubt8tEpItIbE3isEfxwFT0sf3R9zod07Bx6EPMeOvX4l73I57aqj0Ky6UQEAJCQAgIASEgBISALwERSb405FwIHBECenTmCPTx2WiBSc56PDs3ti4L69MhlR+88SfcO22tN4dg6JuKri21t0/RXafKQpVxDLrAMMCBt3wFnpoqvPtPiLerCjkRAkJACAgBISAEhMDvjYCIpN/bjMt4uycB/0h0KVPQq8R1rga8RZ/iXLZst1BSqqZhAc757+JpzRSu63M6hrCgji1JvqP0G4ihVzq6QD9w78SzcxPexkbfEnIuBISAEDggArai1Xz11J+4/PYH+GrrAb3aSeFVvHLuWZzx5E/UWJw+ZXJ494YH+OiXYmodPrcP5rR0JZ8+cxuX3Pkkn67T0om2q8VemsPs5+7gmr++yqJOjPntXpALISAEjmoCEplwVE+vDK7HENAHoosdhXFUPs4fZ+O1bsGz8Q2c1d+0DsEDjhI8pUW7huS1NeD1eruQgyEEfcpAdNtz8Vor8Rbl4Mkehi4ipAvv7mpOToSAEBACuwi4LFXkr/uJD79sILeimYobbuPKMZG7nh/wSdl65syZzbdLC7lo3gXc+eY1jIsJxg8jTUVfMPPOBcz7wwP87ewR9Io0d636ho3MfucHdpj7MfW8qWQb7NTv3MRPb31MVbORyEuPx7PmXT7aEcmZV9zGCGsBa+e/wbs/RbClchvnX/1Xrh0X1bW2pJQQEAJHHQERSYdwSm1OF3VWB06XW0vFHOZvJtjfhF5bCLR9wy63h2pLM/U2B063B4NeR1iAmcggP/yMhvaFgWaXm1prM/VWBy6PF6NBR3hreZNh7/IerxdLs5N6mxOP14Mqo+oPNHf8EVD1lzfYaGpW5cFk0BETHKC1oVdppWXbRcCdOxdP/lL0fY7FkDFh1/0DO9GDKQ597/MxGbLxlszGuSkXT/6O3dXo9OjiB0JZTouFqeIzXN8vR6eLRz/qEnQqk3hnW8R4DPEL8DZW4rWvx1Ochz4yAV2wLCrbGTK5LwSEQOcEAgICCY+IwlpTQ12Rm/DUX7f2Wvmieax0u3AWBpF97wiSg8y0/HVKZfq1J/LF9f/h8yduw+l8nLvPH01GVBeEkl8Anvp1fP/FW8wvuJarZ2Tgb/bD4AnB4B8B9etZ+NUrzFwUR6UjkDNHWFm3pAavfxyhvUYxOtVnTbrOUcgTISAEjlICHX9DPkoHe7iGZXe6yK9uotpix+pw4/F40Ol0mtgJDzSTGhFMWKB5l1gqb7BSVGuhzubA7nTj9nhROsTfZCTE30RqRBAxIQEYDXrtWUVjS3kleOwuVb9XE1X+RoNWb1J4EPGhgSgtpp4p4aXqr7U1a/Ur64NeryfQbCA6yJ/kyCCCzCYNjyq/s6aJ4joLTc0uHG43Xi9a/SV1VpTQy4wN1fqlxiQbeKt34N7xM7rojP3i0AWlYxh4Hbo0O5jD0EVq2Rpa3zOCORF9ZhTEpULSHgkWdDp0wdHQuAjnvE/xOgrw5BUA6egGX4g+5XyMISeC24suPBOd2UcsG2PRD7gcU3JtSzxSSB90nQjk/Q5CCggBIfC7J2A0mvAPUCIiiOCgJFITf41IKmfRl/OpdLrwTjydy88aRUagmZb/gwWSOvksjh/8Mau/+4VPPv2RM47NJjkqlv3KJL9EjjlxMr8sXcqbrz0GNSeS1AD4RxMe5KF62xJ+yAkle/LZnDs1EcOO79hSFkTMqFFMP3MGw5L3NyYPTvtO5jx8F+9u10OfP/Dve08kQa/rnlZ6Zx0la77muae+piAoivgTrufJc/sdoc9yCSs++JjPvviFvF5jOfXMc7jwmIQj1BdpVgh0TEBEUsdcDvquEkjri2s0K4wSPHv+uF+nrD82B/3iwzUrUUWjjW0VDVQ12TWLkG/DNqdbK6ssQMpaFBsaQGVr+RprsyaYfMtbHS4a7A6szS5NHMWFBVJjsbOlvF4TbA6Xx7c49TaotTi0d/onROBvMpBX3aj1p9Hu6xPe8pqqX/Xf7nIxIDGSUH+TJv7aVSoX+yZgjkQXPwZD/D6K6f0gbCAGlWthr80L8bGYvL3xerfhWvAlKp+D2nThQzDsw9tFFzkE3T6e79WU3BACQkAIHCwBVwMVG37g7f+VMvpv1zFxX8vIVSziqx8rcboyOOfqE+kV6NcqkFobD+zLgH4BBC2E+D4ZRAUH0xZQnT/vGeY19Wf0qIkMTPDfo7d+xPQfy9ixQ/j+568pL6okJEQVsVCwZi6zQhsIOukv3HflqQyL2sk389axNSqVgRNmcHLm/gSSih9txr7qA+5/6T1W1Rrpc/PVKNnYbX8+dNtoKF7Nt++9x9qIVPrEn6uJJE9zI3lz/o9/fZKHweTHsbe8wqWD9kD5m18G4Wncxrol7zFrTi5uv2SG9z2TbG1+fvPGpEIhcFAERCQdFLaOX1IubRtKaimstewlYNrecLg9KGGk1FOv6GB21liobLJ3Wl7VqQTR9qoG6u0Oai3Nmlueut/RpsRUlcWOcolTViZl/VHtdVReVaFcAovqrJpLXUpEEFvLGzQXu47qVvdU/aX1NkyGegYlRWrCqrOycv9QEFB/fmPQZ00FBqMjA68nEF2Yfzf+y3woOEidQkAIHFoCFvIWfcWsL79nbUdJDKyV5G3cCDSQv+5dHrxqDTG+HVJfyEs2s2RJHVl1eZx12Z3cMKHj+J6KxV8wv9yNa9zF3DIlnWA/Hyu4VmcQQy74J48OriJs0PEMiAvUXPHyf3iG5555ilnFiYy9+B/cfuFxDNpTKPklMeL067g/bQaeICeb5n3Cj9RQmFtJdEwgkces5osvG9iQEEDh0nUYE0cy9rjj2OVpZ62kYMlHPPBeDqExwzj7jqsZ3zoMl8PG+tmvs7JKj8GYyUmnDiO0B3pYuJqb2DT3Rd5+uxK9OZAVfW7m0kEDfWfzEJyHkT12PKNGzWfxh7nkrFnGivxpZA8KPgRtSZVC4OAIiEg6OG57vaUkS42lWXNVU+5y+9qUOKlosmF1urA5XJ0KpLY6VPmqRrtWvzrvSPC0lVVH1b6yONXZmml2evZbXsVDKfc6VV7FIO1vU+2r8mlRwZiN/rvcBvf3njz/rQnEoM+e9ltXKvUJASEgBFTkK5Vbl/LdB6+zOvJ4TjrtbEb4WsDr89HXbuGHtc0EhaXSf+RI0ny4uZoq2FzyE0UuC66aAP4a3ZFznPpbuZHPnppHqdPNmLPOI2HTq/z9/7bQZG81kfvUqZ3+vIJPWu9V5nxDTdIMpg0oYN23s/m+X2+SE7KJUM9LV/DJl7P5fk3x7hqaK9i6Zi1V9RW4LR42VBmIDYmhPKSZ7Z+sZVtBPc3mbSz57z1cN6v1teYGKks28319PMc1L+Wp2IH0/fNYonDhsG9gzodbQWdC13s6pw0P1dzcdzfYM8506PHzb4m/UhqjUxQAACAASURBVG70MeEdujH85oMJ692P7PRsEsghf2sua9fncfqgQYhM+s1RS4UHSUBE0kGC2/M1FeejYnmUpaUrmyqXV9VIaIAZ877WuWmtTLneKauPcokLMO1/2iwOF9Yml5YowqRvc0zovGdKrCmrU1xoF1wMQEsuoRI7qOQPHSWW6LwleSIEhIAQEAJdIaD+rlgsFsrLy6moqNi1BwYGcuGFF2qxpV2p59eWiUofwvHnXcv5/X1qKlnGRw0reXW2nZi0CZx17bUc4/O4uWIL86o+44VlENT/OE7s24EflfrVb9MsXl5cpv3trPv+Wb6KPJa0TCNeTyciSWujgAUvfsgPdSlcee3lXDXMw9b1dYSnh9ESXQv4hRGX0pv+jpYv/FUbZjPnh29YqsI4W+toSBrHidP/wskJ1czZ/g2znIn0GXEi04/tTxhWKnf8whcvf86OlH6cdPfd/DlqE1uCw1tioTwOnBVrWZYLGPXoh45hRFjPNOgbAkIZfN7jPJtajt5oZuBJKW2QDu3RP5VefZJJT4UN+VvJXZtD3mmDEGPSocUutXedwP6/bXe9rt91SfX/eiUyurop602D3alll+uKSFLxQBaHE6MSPLv+CnTemnLrU3FFSlTtTySpvje7PJo7n4p76qo/dYPNocU+dd4LeSIEhIAQEAL7IqCEkMPhoLq6msrKSk0I+R5rampQe21trba73W4GDx7M+eeff9hE0r76f/DPvHg9Ffz82jvkeDyoiNn/Z+88wKMqszf+SybJTMqkTHpvkEAgEHpvolSxIArWda1r191117Xsqn9Xd13buvbuKpYFlCK9995LCDWF9N4mkzLJ/zk3TJiEAAOiCH4fz+W2r753ktx3zjnvSd2bjWeP8dzS1ZuarZ/x9vYIbrhhIPF+ze51LWOVL6Lou4WsKahk++wV7O96EyMm98deBgdTRwaP64jojZrTN/C/3A2Edu7HwJBq9qcW42oyEOlRSVlJGeZgV/wivImxdCY5fBDXPXQbnevz2LewkhVvzMfo2YHhgwcyoMtABhyfRGNdHQVb1rIDcHbW0W9oP+ztL8Xr3uf12YeoCRnFA3cNpWHVK3yztoRyS3MHocN+w83DOxPqncuq96ez8kA2ZdqtBEbcMpYhPaJa9afdKtjN/IWLW1vHQroy6PKxTOxhb+aT2pXk7F3H3E8WsV9O9f6Ed+7BwHa4qghFebo0kJ6ejpPOFafMYoZF2blGHvieZz7boMU7a/PQ/gvnsrtvZ3gHE57H3yQzVrzN9JVHyHEfwKTre+OeupBl6w+Qrb0aBZAydgIjB3clrCV0zJeIuBgi4v0gM528zAOkF6FI0gmQ1dEFRkCRpPP4AMTS42gRYiJ/HB0tTTRpcUMO129q0r6Zc3QImcmZ3Pjajq2toe1Fda4QUAgoBBQCJyEg5KaiokIjQkVFRSft7clQVVWVJoqj0+kwGo0EBgaSmJio7f39/YmJibnICZL8wbFi3T+H5/9XQa/eUWzekklj6BBSYlxx1UFdzUEWvvctlX5BPH5lFwI97OKUsjM5aK6mvKSaivomrE4uLUIObYEv3LOAH778N++s09F12NUMrNjIu5kexI8ayVD3bexZ+xEz0ofQYE5i9GUxVOzNJKcCOmOmKjuT/aLep08kpo1xpaGulsNbV5KHM666aEb2jWk1dPnOGbz91nLKkrJxyVhE9saPmL2tjMra5mqBq4/AsQnUlm5j+UfT2ZyejwjvQQxrD+bz2LO3M6bnCaJUsPsHFk//mLenrWd9av6JsQI7smL1Icpun8S4iT1opkqV5OxZwn+ffZUPZ6xFSySh9yW4Yy+Gd6wg90Tr5qOGWsz7F/Daa1+g03swOOpu7j8eP3Zg1tP899sFvDpjB5Y6e+teMOuyqrD89QFGd/LHywXytvyPLz9cxQ5LCnt3dUF/eB0bdx4lv0aG8aPT5qMU/flhJo88QZRM/qH4B4iq3T6Ky/LJK6iGGM+2M1TnCoELgoAiSecRdpHrbk8Vrr0hJK+Rq87ZYf9lkQMXTz5HcxRJLia9i7MmG97e+K2uOaG5/GnzaXXj9CeSY6m9nE+nb3Xp3XWO6oOL3ohzWPdLb3FqRQoBhYDDCMgXXzU1NZrlR4hQ202IkFiMZCsvL6e+vr45PYRej8lkIiAggISEBIQIySbX2h7r9ZdCbrNGrPX7mf3C+xzs/jDP9/ie7duzqLND2iflCoZ7fMqMd+cysk8kw2P9sK28IjuDPHM1lsCejL1lNAM7hbQTx1JN+oZFzPjwFRbluJN45b3cNtSdvFkbwcOPqF5juXX4SLZ+8w3LDtTjN/G39NFvYsamQ2TliD5OPbWV5RTq3fGPiCOqlZkKzR2wrETohgtOhOKvBULZLcB2eGwFX/yngJCrHuH+P/vhlbOcd77bQO7mmXxYvo+cHG8GTJrMA/GBlG36inlrUtk451O+SEkkLiKYPkF6qo+uY9bn/+b1L5eQ4zOQa+69k14iT156iPWrlzNvzoe8WFtHXWwYv+sRTGX2HpZPfY2PZqwlJzCG/pffwPgEKEhbw7RvNlNgm5sD+6OLX+W16Ulc/fhTdHZ3bVYdTF/MG9M2sWHaO7ya3Jfk+4aTENBiHoLiHSycmk3PCVdx4x9jMGQuZ87SjexdPZ3pXVNITupAWGxzfYN/IMH+Qfiyj4KSMrIKCjVZeQempqooBH5yBBRJOk8Qi7la8h/tzS11qEdxm5P4HyEmjhTJgaTXOTtMkiRuyc3F+YyudjK2uNdJ3XAfx7+9kfUGGg0Oz9+RNV6sdZwjeiKbKgoBhcCvAwEhN0JyhOwIEbIRH/u9XBdSJDFFQp5sViEhPXFxcS3kR4iRXLPt5djbWwQAHHV8vvgwb7LWUbDxS/6xIYQHv76dAZtmomu7XJ8UenfT88mXU3lrXj8SbxlEjE8zTcrOOojZXEFAz/50iwvFdJImRDUZG2bxxRsfsa4smP63PcJNo/tiPPQtS/ekUeaeiMHLRExCEKWDSyjQN3L5lKHUrzmAu24DW1NzmOxZSXZGGni4o+8UTWtDUiONjWWUaqnsnHByCsR0KpKUm0vANY/y6BNPMzHFD6+8KFLXpTGrxMyBA05c/pvb+MPTUxgcH0jF7FKOZuSRUZrO+p2pHMm6jD5BHmRsnMX8RRtJtSQw+rY7+ePDNzJII0mHWfCFK4V5H7F521qWzLmCa3sMoHzfJpZOW8NhfIiLH82dzzzNXR2bKNg5F5+yEl5YoDngOfTBiR/7FE8GdeXax8fTqYUkhbJ16T4WVOSzfucRKqoHgD1JIpCeV/6WB/98H1f1iUGfGkpVzjHSjx0g9Wg6GbllEHvcPdDdA293D821MKOkjLy8QqqJwfG3EYeWoSopBM4JAUWSzgm2kxvJH7QIP08OF1Zo0tsn12h9RefshOQmEhW6ytp6LWFr6xqtz/w83PB1d9PimCTp7OmK/K0xeeq1+rnlZiQ+6UxFrEJJIUYOFVUg8VJnKgFeBm0MWYcqCgGFgELgUkNAiI24vdnc4IQA2R/bW4mEMElckRSDwYCfn59mBYqOjm4hQOIyJ0RINjn29fXFze2kt/tfJIwFaRuZ+eGLHLbX+K48xp4N6UA5uYcW8dmLFSy2m721uohD68UtzM5NTrtvpcFyiEWvbsD/vpe4o7+J0s3t/R3xpXvvZNy/W8wP7y3i7su7EuoThJ5s9m3JoKzYi563dicuxK/dpLKNVjdCU8ZyU/crGDE8hQinwyxYs4qlO7OpD+qOe1UpGQczWbNuO9le4wnz8qLENwQv9wI2L1/L/g569u1Kw82jK2FxJ9zempfYTJJKxOiBkKQg/E+Tgy752t8zrqsfXvK4o1IYEOnJikNQXZfI5bePpld8oEYKPDv1ICpgHh6UUlJZRZVFfPOqOZqaTk5GJTiHU3V0Cwv+m8fK41iXHc5F02YqKqMiPZe8uiKqsg6x7zDgE054rysY07k5ECkocRDXTx7Ahwv2Y+ewd7yn9ncdxj/N04O28e27b/CDpZ5mh7ujlFfWnSZ+OZBeE65iSPcY/GXN8YkkBAQTwAEyKquprDkemCVDGtzxcPdoJkXmGiyV1UgIkyJJ7T8PdfXnRUCRpPOIt7jbdQjy5mBhBbX19r67rQcRYiE5ieIDvfE2uHG4sJzK2oZTxiiJ+IKQEn9PA64ukszViiSqba/InxpbfSFWkty2sKr2lPFGUl/vqiMuwEhsgDeNNHGkqPK0RMnfU0/HIB/EBVAVhYBCQCFwMSMg5MYmimAjQXIux0KERERBNiFJ4krX0NDQYhUSMhQZGUlKSkoL+RECZNvEKuTp6XnRW4WsdRaqK8oosxcNqqqkWks63khDvZmKsrJWL7YN5opmGW+rE42l5ZTLO7vtg6IDY5cbefpOkdLmuGCB7eaJfWyvoUS4ryNrz3x+2DKJnhEmIhr3sWtPBaUuvZjcPY7Qk81I2it2SFQsEbGF7N6zmK/3LIaaLLYt20BauRPOzofZOecjXlqfz6ble6ntZ6DbZR2Z4B9BxzA35m2ZxcJe3dh9wA0fnwj6J0ecmNSPPvLFFOqCi5CHtt93Gn0IdNNrCWlbZKDMBeTllVGsBSztZ+102U4zidpaLFWVzZj6GvFMiKZl9i6uuPmatLglR0lS+pLXmbl8Ne+99gOHWkjSacZv75aXEW+DQVvXSbc9PPB096AdPYmTqqoLCoGfGwH1lnseERdrUsdgH8kTqyndifqb1U45QQiJp96VIKNBIxlCqjzdXLS4JLH4lJmbCZCtiS3eR+fkhKjb1VnNGnmR67Z7NrEF6dvmniF7qV/bYKWhqUkjX9bGRu2+rZ0sW8iaj8GNUB8Pjdy5uehIDPaV78W0hLTl5tpWkuaeehd83fVaEtxgo4fW/jzCp7pSCCgEFAI/CQJiFRLRBBsZst8LGRJ5bZuinM0qJEIL7u7umsVHyFBycrJmFQoKCsK2CRmSYx8fH1xcLrU/pwZCkoZx9R0wJHI4V4y7mh7Bdo9HJMDfzmP2hkNEdr6Gh19+sJUEuORJOrA8kajVFfgmhRy3QEh7Ha6Grkx80YFkpdHxdHFzYwe7mLtyP/cP60xElYg2mHHtOYDuccHtuNo1z9FaW0VZUR45osJAKUc3bmTj+mN4delNv8EpeBfvZOPi5ewwR9FJ10h9dT3G2EiS+nTHMHce0z/KIKfED1P3gQxL9rVb+M982NiI/P3W/Du84+jRvy/9kkKwiwA6PqEgEvomaQl9NS/A8zHNsh1MfeEJnl9TR521M2PuGUEHDzdcOMa6zxaws6yK4zoU5z6axUKNpYbqc+9BtVQI/GQIXGq/1X8yoBztWGKNEoJ9tPxBhZUWTbZbkrsK8XB31WHy0hPi7aHlOhKXcxFwiPE34uehp6iqRkvmKjmUxFIkIhBikZKcR7LZFyFOkp9IiJZYjsRiVF5Tr7nWCUGSTYqmWtcoRElSOTjh56nX4oiEIAlBC/RyR2S/bbFR0ldSqC9F1bWaK6DMQ/Ond3bC10OvxSEZ9a4thMx+TupYIaAQUAhcKATk91Rtba1GhMrKyrDfhBQJCbLlG5Jji8Wi1Xd2dtZIjri/hYSE0KVLlxYSFBwcrB3LXtzkRDTB9mXUhVrnzzeuB1F9r+Guvtec05AuXkEkTXic1yecU/PmRn4mAnU65EWlrr5B84jIT91KeqUTna/sQfwpXO2ksVeHYUyRDbDkreCz/8viUJY/A25/gj/d0YvyBa/zwo6N5EaN4MY77+Y3A8I12eywpD70dP+MT9YU4BaURPLAYXQ7iSM54eRsxEeLQ5K/ryWUtTKV/Yg1t23qFURIkC/+nnDEJY7+V93H0w8MJaxtPdu55QiHPTybLTOVZmoycjXXOo3fWhuor6yg2Fb3tHvJYr+KJRsasVohuP+dPP3S/fQzuePCCp6Zu46D54MkmaupMldTKXMx6NF7uONYtsbTTl7dVAicFwQUSTovMLbuRIhSuK+nZqGpqq3XrD/yh9VL76qpzYnVx74IYZEYItnkG6Pq2gYtMa2QJHtLlH0b7boTGukJ8DRQUGnWSJLNsmSrK2NJ/+I3LUTIx+BKpMkLdzcXjSTp2kk066JzJsTbnWCjQbMkSZ+ypuZ+bD2rvQ2BxvxUmoqP4hSUgHNAB9tltVcIKAR+AgTE3U2sQmLxESIke9uxuMQJEbJtcl3IkLjUeXl5tZChTp06tZAfIUBCjmQvm0huC3FS5ZeAgBFvfx26hMHcOKwLoT4N7Nm2nXKnDlzRI5ZgP0diuizkpOZQXGnBq4sv1QX7WP7FGrYvXcj2ygSGTp7AdX2EIEkx4OEVQHAUkAFevt4k90rmZE0GHTpnfwI07QErjU07OSAxQD+Jfk8g0Z0iCYnygNTDbF+/kU0jOzGmU9Bxa1Iluft2sfegBf/eA+kRbiIgPJbECNhyLJucjctYevQybgprIH/PKr6Zu4Vjx1d7Njv3bh0Ic9XhTCVpi5eTWm3+8VYkmYDFQpVsgIe/H0GhQe0oFZ7NTFVdhcD5Q0CRpPOH5Uk9iWubxBydTRHSIsIM2WXmFmvQqdqLtSi7rFpz05PEtBKrdLoiAg5iIYo0iX/wmeclxM71JMmh043w67zXeHgVDTu/w6XvbxRJ+nV+BNSqzzMCjY2NWvyPkBwbIbInQ0KC8vLytE3ihsxms1ZfFOREGU7c3yQeSHIL2QhQaGioRoJkL1ahS8897jw/hF9Ed9EMuHUyN+vG8OiEroTq9jFtfzW6hLH0iAvCIY5EA02+XbnsmnsIyVrP/6a+xzObD2gy2N7xfbFaijmyeQc+A1PwrznK3o1L2ZUXRGRkHRXVJRTsW8euPuPpFtQaECdnZ4zeAUARTY1HSD1cCT1/isgaI3F9hjOg7xq2ZGxmw4IvecutgmND4o7HgBWTtmw+6w4HMua5ZHqEBxHWIYVBozvyw8cHyT4yj09fjaK2Uy2H137PyzMyMJmgxJE0jd4RxAQ5sfYYpC/+gk8+zyPGs4J1b/+HRYVlmsBCa1TO/qy2pIC84gLNuhVr8iUyWDBVRSHwy0BAkaRfxnNomYUoyxVU1mhuei0XT3NQaq6lqMrisKVH3OeyS6u0uKhfj9vIaQBUtxQCCoELhoC4x4mCnBAh+00IkbjECRHKzc3VLENyTeS0JVbIw8NDI0NCiOLj41vIjxAg2yZWIRFNUOWnQaC+oR6LuUVe4KcZBH8G3vtPBh7v3bx9DWvSrERf14P4oON5k0oOsHpLKhkFnnQemELnuIA2AgFexPfoRmSkJ/o9Fg5uzKS62JtuPTvhbznGgRnP88iWsdz9xzvomf4/PvhqARmxU3hwcA1zpk5j1huPau7sv5kwgPHJJ5iSi6ue+B5DCeA7Spqa2KmZklJ+EhyMnUYw8aZ86mqd+GrlHpZ++gJLP7UN5U5QbGcGT5lI19DmSCWfyK6MmHQf1+55h+83HmDJ279niasRU0x/rpl0PRx7n+l7be1PtXeCoEHc9LurOPD2LDalz+CFh2ZolU3JV9Atbh07jlb/6FiikqI8Sook35QH/r6BhAb+FETzVGtU1xUCp0dAkaTT4/Oz362w1GmxSBLH5EixxR+JZUhkvM9U6q2NlJjrkL0INaiiEFAIKAR+SgSE1Iilp7KysmUTQiTn4h5nI0JChsQqJNfFRc6WV0jc38Qy1KFDB40AhYWFtezFSiQWI+Ue91M+wfb7ttSYKStzLLql/R7auVq8k8XfzeCQhyvtOTyWrJ/N1vwGOhbtZdUiM/vEIeLoYt74fA4b9gdx5f0P8cgfJjLQniiJVPnefWxa+j0L127nUG0SV/3hQSbdMo648q0snvYWry0+zNcvPcsmSxZFKbdw46Q7eLSvEwMCmnjijWlM+8fv2LjjPl6+Lhl3Ywf6j0smSK8ntN9QUviOJY2NHEo9RBUpLa5iEhN17XWBmGut9I/xQN/y59mD6L5jmNCUTJGlF52DjC1JcnEPI3noaK7x6URlUgpxAbboHCOJo+7m99HRRM6YyaJdJXbg+ZI4+Equv+1KutoS3hpC6DT8Jp74uyemD5eRLbXdg4kZOIUHrgkm9csydAn+hCQf13TXuWKI6sfkyXU4ubjRuaPX8f6DueLJt2lq8uTLvbXUHc8OEnvdXxlf9S5frymkuGsHTB7NsoemxBGMmhBKYmk4veMDMLas2ZeYPsMYYzbRLbEXCcG2Ly8qyc3IICejGNw7ExzekShlSLJ7turwQiPQ8hG+0BNR4zcjUFXbQF3DmfMU2fASsmOut+Kld4xUSTuJexLXO0WSbCiqvUJAIfBjELCJJohVSEiO7G3HYgES97icnByys7M1JTkRUpB64lYnViEhQrJJXiEhQbYtPDxcOxYVOVGaU+USR6D+COtmzmCrS3sUSdbuS8pgX8jZzLKZm1vACEsazMQkIO8YmUcKSYkzYj60m9SDmRQe3sg3izaxu8iNwaN/yws33MTYzgbKc1LZnZpBQ3APhkWX8sNKC6E3PsmTD91Cn+NBSP0mP8UrOmeeemcB6xd8zCuVw+geN5Y4IUnOrriGJtM7GpZkNdK0cys7qicxyLM5QXvQFU/xyRUtU7Q7CGDQfa8y6D67S7ZD315M/ksvJtvO2+yNiaO448lR3NHmerunhmASR97DqyPvOel25GPfMMr+qqsn3oMe4JtBD9hfPX4czKinPm9dX7vzHwb/tnX1jhOe5Z/tCnVEM+y+pxjWds1VaezZfYg9h8G9U0cSuicTbyN6rbtWZwqBC4KAIkkXBPZTDyriCmcTMyxxT6JaJ/8cLeJmJ+p4qigEFAIKgbNBQEQTxCpkI0Hi/mY7Fvc4IUGyCSESK5EIK0huIUmaKq5vIp4gKnKxsbFERERoBEiIkI0MifucsgqdzRO5cHVdvAKJTRnBhAn1dOwdw0kCcGcxNY/YgYwb70vdsD/x5b398Db82FeTcvbsXc2C75ezW4wuHa7i6acnc3NfmyZcNaUZO1g5/WtW5wHe8Vz19M08fHPvViINbqYYBtz8PB+EdeKFufCb5x9jREtCXVfcDD0Yc0dn3v9bGhVpc/lh59MMHCB5sc5i8b/iqtVpe9h9cC+HMJDYMYHuyXEojvQr/kD8Apf+Y38T/QKXdHFPyce92W2uvKZOk+0+02rcXV20nEkiJe5IEVIlUuTKiuQIWqqOQuDXh4BYhcTdTQhQ201IjxCgY8eOaZtYiCTPkFyXL1/EKmQjQ1FRUQwYMEAjQEKIbJu4xwlpUuXiRsA9PIUrH3mXKx/58esIvfIlpl/54/s50YMPXa9+jL9f/diJS62OPIkZcCOPy9bqejsnnsF0uPIxPmtnfi4GD3pP/j1DX7+b2ZX7mTNnF0/1749RsaR2gGx7qZq0PTs5uO8weCeTlNybXsqM1BYkdX6BEVAk6QI/gLbDS1yRydNASXWtliup7f225yIr7uvuRr2WcO7MLnd6F2fCfFUi2LY4qnOFwE+NgMhQHzx4ULOUiCXFYDg5HeRPPQdb/0KEbFYhsQzZb2IZkjihrKysFjIksUJiGRKrkLi9CRmSTZKs9u7dWyNAkZGRLXtxl5P7ShzGhrjaX5IIOLviFjWMG28ZSdV+ZzDnUiY5mrSkG5fkis/jokqoafAnNHokI0eOYOzYgSdiqs7jKKorhcCPQUCRpB+D3k/UNtTbXZP1zis3a3mKTjWMuNmF+Ljj76knv7KGwgrLKfMqSR9SXwhYqI8taPJEz/LSJBRLXmocsUnZ6otl6tdenLxDcQ5NxsnrhPLRrx0Ttf7WCAjx2LBhA6+88godO3bkL3/5i+Zq1rrW+T+TmB+xCgkJEoIjm40QiQVIiJBsmZmZGjESIiTXRTRBSI4QIrEMiVVo0KBB2l7IkG0TkiR1VVEI/DoRcMbVvSOT/7PklHFEv05cHFl1JIPufFrbHKmt6igELgQCiiT9SNSFLNQ2NFLbYEWOm5O2OqF3ddFISdvuJTGr1BVxBqkvpETikPQukpyumXD4euiJD/TWsosXVNRo+Y9E7e5497g4OWFwddFkvOMCvAk0GrS8R42NUFxt0SxQNnU84TCSe0lc7AK9DCSF+rao4NnmLrLgdVYr0kaSxhpcddom87Ivtrlb6prry2pb6sv8dc4OESz7Pi+FY13Xq5BNFYVAewiIFWbhwoW8+OKLGqG49dZbNZGC9uqeyzX5Oa6vr9cIkBAiGxmSY1GRkxghIUE2QlRQUKCpyMl9IUFi0RJCJG5w/fr104iQkCIRURAyJPFCrq7N6lXnMj/VRiGgEFAIKAQUAhcjAookneNTE4JQ39BIWU0tueVm8ivMGvERy4qvhxsRfl6YPPV4urm0uJwIGSmvqdUSxRZXWTRyJTzEX7PueGhWHnc3nRZjFOjlrsl0V9c2UFRViySLFVU6IVJeBletboy/USNIMmag0V2zOlXX1Wt5lqosDRrJ0umckDinIKOBjkHeCAGTIoRI5p5TZia7tJrK2nrtupAqIVMRfp6axckmKy71S6ot2tyPlVZreSMEA5lPkNFdqy97IWPKxeYcP1Sq2SWFgJAXid3573//y3vvvaeRjr/97W8MGTLknFztxD1O8grJJgTHtknckIgmZGRkaGRICJHEDUm8kJAksfQIEZJNhBHE1W/48OEaGRIiZCNDIrOtikJAIaAQUAgoBBQCzQgoknQOnwQhB3UNVjKKq9iTU6IRDvtuqusaNDIR4u1OtwiTZuURa9OB/HKOFFVo5Me+flVtFZmlVUSbjBqRMRrcqKlvQNztzHX1aMTJ2UmTyxVCpHfVadfzKswY3V3xdHPVcivJeU1dA0a9q2bhEcuPkBiDiwtVtfXklJuRvt1cnCmssrAzqwTJy2RfxFpVUGmhsNJCkclCcri/ZuUSErg1s+ikOCkhT0ISZeyEIB/iAr21+SgvPHtU1fGvDQEhNEeOHOHvf/87ixYtinDZIwAAIABJREFU0gQMXnjhBRITE0/rnibESmKXbGTIthdCJLLZYg1KT0/XCJEQMJuKnLTR6/XaJu5xQUFBDB06lJiYGI0E2faSYFW5x/3aPo1qvQoBhYBCQCFwLggoknQOqFmtjWSWVLE7u0Sz1pyqi7yKGhqziukRGcCRokqOFlfSYG0/B5KQk/TiSq2raJOXRprEYlNvbdJIirjj2ZeGxiZtDnIt3NeTY2XVpBdXaS55olzXVr1O3PuE1AlxivD1bJcg2fcvRDCzpFqzCkX5ebVLkFrVb4IDBeUaKYsP9NHc9ezvq2OFwK8FAXF327lzJ3/84x85evQoN954oxaDFBAQ0GJlFRIlxMZGiGzHErskxEeIkLQVq5BNUlssRkJwRBlOCJHJZCIhIUGzDAkJkk2sROIeJ0RJFYWAQkAhoBBQCCgEzh0BRZLOEjshM2Ip2pdbelqCZOtWrDJ7cko1dzwhNmcqGcWVmkueuOZJotjTFelPrDhFVRbMdQ3HY6JO3UISyArxKTXXnWRBOlUrIV6itFdbbz1VlZbrgo0QMXG707u6/2zxSbbg9JaJqAOFwE+IgNVq1ciNDCGWH/si7m1Lly7l97//vWb5feKJJ5gyZYpGbiQWyEaG5NhGhIQMiYVILEMSvySfZ4kBEjIkeYWE9IwaNUojQEKCZBNCpEQT7JFXxwoBhYBCQCGgEDi/CCiSdJZ4NjQ2aq5lYplxtIjFSeKCbMIMp2snxCejpApDO9ag9tqV1dRpZErEFkRE4UxFCJ643QV4GRwiMfUNVg4W1BBsNLR8C366MaT/EnOttt621qzTtTvbe7Zgdfl2XdS4FixYcLZdnLf6fh6ueLu7UGaup7ym4bz1qzr6ZSIgJEaIjewrK5utv/J5FNnsqVOnInFHYsn5v//7P43gTJs2TXO9E1JkswyJ+5xYhVxcXDSrkLjBJScnExcXp5Eg2csWEhJyTvFLv0zk1KwUAgoBhYBCQCFw8SDg1NT2q9CLZ+4XZKYS87PxaIEW0+PoBNIKyony89QU6c4kmC1CDaXmWk38QXIgnalITFFVbQM+Bjc89WfmvGJxEpIUY/LCpY16XXtjyVxkE5EIiYdypEjdzqG+ODJ/R/prr468ZM6fP58bbrhBy/fSXp2f69o/r0vk4ZHRPDfnEP+Yf+TnGlaNc4ERcHZ2Jj4+nl27diHWpTFjxrBmzRrN+iMiCDbRBCFCssk1sQBJGyFAtr1cMxqNDn0JcYGXrIZXCCgEFAIKAYXArwaBM79V/2qgcGyhYukpqq51rDI0u+Q1ndifyXyjyYNbRR7csSHEJU9c4Rr1Z24gNWz9OmoHa5Y2d2wutloa7z7zdGzVz2kvFiRJzCnf5ku5kIp6J7ij0wWdxzkBqRqdMwLy2SsvL9fc5KSTvXv3apLaEm8khGj06NF0795dsxCJYIMSTThnqFVDhYBCQCGgEFAI/OwIKJJ0lpCLy5yfu5vmUuZIU7G9yEu0ZoVxwBBjcHHW1OdOvHiffhRxsRO1Ogc87TR+1jyX5jmdvufmu156F03a3JG6tjqu2nwcWKytwTnsfX19NSnl0NBQLY5D3JIuVDEavbWhfX19NFnlCzUPNe7Pg4B8CSCxReLmKXmFOnTooJH1JUuWIK51M2bM0NzqRIJbCJMQJBFakFxGQubFAqWKQkAhoBBQCCgEFAK/bASUu91ZPh+x3BwurNCU7RxtKsIKfh56h2KSRFK7urZek/luq2jX3nhi6ZE5SQyTI+5zElMlLnciBe4IjRE1PHEx9NC7OlRf8uH2jQkizNfTofW2tyZHr4kl6ZFHHsFsNrNixQpHm533eg3r3qdh53e49P0NLr1uOu/9qw5/WQiIa92OHTsYMWIEzz33HI899lirCQqBkhi5L774gmXLliFWz44dO2quoeIeGhERoZEmiUm6kBbQVpNWJwoBhYBCQCGgEFAItEJAfaXZCo4znwgRCfP1wFXYgIOlW7hJk/F2pLqLsxPR/l74urs5Ul2LRQr1FjW51hLhp2oscUJdQv1wdPpC1LpH+CPzcqQEeLnj7aBIhSP9qToKgYsNAVGlu+qqqzSrksQrSa4kIUQvvfQSPXv21CTBv/vuO8rKyk5Sx7vY1qrmqxBQCCgEFAIKgUsVAUWSzvLJClXwcHWhc5jJoZZCdrpHmIjw83CIaET4eWkJXAON7ujO4HMnt0N83EmJDNByH51pQkJ0go3udA0zERfQ7CJ2ujay1j7RgXQJMxHgaTijcIMkvU0I9sZoOLPgxOnGVfcUApcKAiLf/Yc//IFt27axcOFCbr75ZjZs2KARpWeeeUbLgXSprFWtQyGgEFAIKAQUApcSAiom6RyepliT4gOMmgrCruySU/YgstySM+hocRUebi4aeSivqW83v5IIQkg8krjapeaV0SSCBE5gtTa167Ymbnl6F2cqa+tJyy9D3OhcdU7U1FtPkgK3F2yoqW9gS2Yhzjhh1LtSbqlrl/wIoYoL9EYkxkXdLtBoQOYox+KC17aIhapHlD9C7hxVwWvbhzpXCFyqCEgc0uDBgxk0aJBmUVq8eDHR0dFaQthLdc1qXQoBhYBCQCGgELiYEVAk6RyfnhCljsE+BBgNHCms5FhplUYipDuxqNTXN2Kpa+BgYXnLCE2NTVrQttyvrW/UyIZYg4R0NFobqQONhNgaWJuaqNfIkrNmhRJuIgSkwdqItbGROiuUVJ1Q2pP6Qp6caNLikyTAXKxB0n9dgxUndBRWWmzdI1RHrFW+Hm44OzlrREvc63Q6KKuq42CBzF16kJrNe7GMubu5NCvqNTXh7upCpJ8XwT7uSF4kqaWKQkAhcDICEn8km8lk4vrrr1ciDidDpK4oBBQCCgGFgELgF4OAIkk/4lEIYTF5GvB119Mj0l8jLpJMdd3hfBqE0Tg5tUhua8M4OR0nLI2kRJgI8XbXEsceLqrECatWxd5GI/2LIIMo6nUM8ibS14us0iqkvrO1mY60rS8kRyxYkqso0s+TzJIqDuSXazmaZAD7+rbz6jor3cJ9CPf1JKO4gp3ZJXbztrVo3otlSZbWJyYIUb7T1LqcLqwEtwbcBfzPpd8duPS5DZzVj9MFfAwXzdDyMyMxSqooBBQCCgGFgEJAIfDLRUC91f3IZyNURUiMWFrEKrQ5vRBLfTPhOVXXYu3Zm1uKpcHKsbJqzSpzqrpyXepnllRTXlOHua5Zze509SVvUm65WbNkZZebTyJGbduKlWlvjhCjRnbnlNoRpLY1m/MsiYvenpwSekUHaJakk2v9yq7oXEE2VRQCCgGFgEJAIaAQUAgoBC4JBBRJOk+PUawrueU1WoyQzfZyuq5rGxrZm1PqsARwlaWesppazS3uTEpzMn5Jda3mWic5lBwpEsu0JaPIofnIWvMraqisqddU+1QMkiMIqzoKAYWAQkAhoBBQCCgEFAIXCwKOvUFfLKu5gPNsoomjRRWntcLYT09EEMTKcyark62NWJ3KzHVnVb+42oLkUXKkiPVJBCYcLRLnlFNu1nI0OdpG1VMIKAQUAgoBhYBCQCGgEFAIXAwIKJJ0np6SWFfsRRHO1G291aoRHhFbcKRU1TYg8U6NTY2OVNfio4RYOdK/CDzUWcWNz3pG1zz7watq6zVXQPtr6lghoBBQCCgEFAIKAYWAQkAhcLEjoEjSeXyCjhAS23DiotYcyWS7cvq9m85Jk/ZubnX6ura7wr+cHOJgJ2YiR44WIVeqKAQUAgoBhYBCQCGgEFAIKAQuNQQUSTpPT1REGwK9DA735qpz1lToHI3nMbi6aGpyLjrHaIyrszNGgwuuLmdW0ZK5S+ySu+uZ69ov0FPvqnIiAQ3b/0ftN3dj3TvXHh51rBBQCCgEFAIKAYWAQkAhcJEioIQbztODEwtPbICRwqoTeYhO17WQo1h/o5Zbqb3krG3bGlyd8XTTI5mPHKlvNLji66l3OIZJpMPjA30cjjESqhYquZF0imdTU0pTaSZNlhM5sdo+P3WuEFAIKAQUAgoBhYBCQCFw8SCg3nDP07MSa0yIjwdGvWNS0EIuuoab8PfUO2SNCTJ6kBTqR6DxzNYqycPi72WgS6iv1v+ZliiELcDLQEqkv0PqdtJfsLc7Pu56nDX58zONoO4rBBQCCgGFgEJAIaAQUAgoBC4eBBRJOo/Pys1FR6+YAM0t7nTdCkHqEuanJW9NFqLkZTgtURI3PkkmGxvoTccgH4JOQ5SEIEn9ziG+RPp50SnEFz8P/SmnYyNIPaL8CfPxoFuY6YxESebbOdRPcxc8ZcfqhkJAIaAQUAgoBBQCCgGFgELgIkVAududxwcnLmj+ngZ6RQdqcuC5ZWbqG0+IGwiBCfDSE+NvJNTHAyFVEgvUPdykyWlnl1VTaalvcafzNrhq1qlwX0983d1w0TlrliRXnQm/smotYazUlxGE7IgVK8zXQ9vEyiNJboON7kj8k/SdV16DKNJJkfrSf7ifJ2E+nvi4u2lCEtEBXuh0kFFcRXF1bSt0hNyF+noSH2DUXPmkD1UUAgoBhYBCQCGgEFAIKAQUApcaAooknecnarPMeLi5EGMyUlpTp8X5iFuaj8EVL4Mbnm4uGuERiiHEydfDDXc3F404WeobtDglnZMTBjcd0o/EC9kIiYuzMyZPPZ56FyL8PJEksNbGJo0Qubu6aPWFeLXU1zlrrnReelei/Ly0+hLTJP24u+mQNnpXnUaQBAoZK8pkxN/TnYqaWi05rvA8IUhikZJxZa62/s8zfKo7hYBCQCGgEFAIKAQUAgoBhcAFR0CRpJ/gEQiBEFIiZEjEE4SUiLCDWHTEutO2CFEyuOq0rbHJTUtIK0aaUxERuS7kRjbpW5P6PkN9IVuyOVJf5inETcQf6q2NSKJcIW2uOh3KeNT26alzhYBCQCGgEFAIKAQUAgqBSw0BRZJ+wicq5EcsM2dTNGJ0Mo86ZRc/ZX0hdDrns5v/KSeqbigEFAIKAYXALwABcbmW3+sqJPkX8DDUFBQCCoFfMALqt+Qv+OGoqV0cCOgSR+E6+q/o4odcHBNWs1QIKAR+xQg0AI0X3fqLVr/FH373JJ+szaC4rr3pl7Fz+sv8+5MZbMwwt1fhV36tivy0Obz34J+YlvpjoNjD1N/dwa2vLeFYqX3KkzRmPfM6M1YeprB1OPPZD1a0j6WfPsNv//xPvt1edlL7uuKjrP3kSe79/TusLjrp9q/8wl6+efge3luYSdE5/xjksPnr1/nT428wdWOOHZ6FpC6ZwRcvT6edx2JX7wyH1lpqN77NpOtv4Km5ediF7rc0LFj6T+65/Um+2llEpfzKukBF9+yzzz57gcZWwyoEfhQCJSUlLFiwgPr6em6//fYf1dePaezk4YezXxROBp8f041qe5Eg0NTURF5eHp9//jkjRoxgwIABF8nM1TQVAoLAxWhJKmLte3/ltS8XsnbbVmoCetAhIghve+HWjOW889Y7vD9tKRvW5NEYEEJEhyA8L/hD38NXDzzFa1+k4z2wE6FGA2fjwlOxZw4fvfM6r723kSI3fyITQ/A645os5O9bxlcvPcUnKw5CzBASTGAuOMKq//6VjzfkUxvai37RHmfs6aQKhct4/fE3mLFxGzs3VBM0MJFQXw9cKWHjJ6/x2Q/z2dEQSceoUAI8HVxp1SHWTPuOuesK8EzoQKA1k22Lp/PBe99z1OJCeEQYJavf5e9T1+IUM5hQyz6WfPwnXpm2jbTMTKr8e9H3XNZy0uIutgv5LH7xQZ5/7xuygy8nKcSAm86J1GnP8N6ctRxw6US32EC8DWfpEVSZxrKpn/PBpzNZuz2PWrcAorpF4E0Nx7YsYtqHb/NDehUuQV3oEnrmtDRYzZSlzeWVp+ZQ328QcR4N1B+bz/N//IA1+1Px7T2ZlILPufNPr5ATMpquIW4c+PYB/u+TlWzasZUst850jwnAqD/LdZyHx+ngJ/g8jKS6UAgoBBQCCgGFgELgAiNwETqQpC3k85VHyS0vIjGmL4nhARjdStn29btMW1lG7MTb6V68mB3708goT6J353g6xPojFKDqwFJmzJzNIa+hXH3tdfQO/bnhb6JgzxxmrV9Cas0qYrzdcSybYvM8LXn72LYrlSMFnpSGRNK1X3eCg8+wBkshmdsXM/WTb9mi20pGdQSBz91KF+9w4jp6cXjBYtKr/+8MnbR/u3DTarZbarAU6gh9IFHLmdi8nghG3DqcH37/LrNefRKL5Tn+PGUonYLtmWz7feLmRl3pXlZMfZ/lx9L5zfhkDHoDbg16GoQSmtNYO/MtPl/oRWa1D+axbmxfXUSjzgPnwG70iHA/RceX+OWCdXz70TS+zTCzOiOSgPef5qpOgXTpHU/RvBUcyLdgsZ79z3tV+iEOZB4hp9SZrqYQwpJCjxNzH2J79KRHj7n86/M3eUMsiQ8/xA09fE8PtJMTTboG8ta8yV8fPszWO5/i4RBv/IGDVSaCgmDPl68x7X/7WJUeSeCHEzk6v4AacxGuPl3oHu2Pp9vPT5BkUYoknf7RqrsKAYWAQkAhoBC4hBCQV9qzf3H66QAoYMk/nuTbg41YYyfxzO+vINrD1W6GB5j92ues2VNEdQMUHdrK9Df/wmK9mczNK9l2sIau9VY2F67nwOEKaMrnyK45fPTPtfwXsOTvZ8fOXRToV3GsookHfjPpZyZKXnibnHHWpdNkuod+/ULw1UPpps94c8YmMtwGcecdY+kT60zq9I/4btU+vIbey8TL+xEv757ZHpQW5XOkMZjg8FACHTD+lGelsWX5LHaW+xOUPJjhoxMI0oNB50eHHoMInLWV/RnZQMJZPtYiNi9YRa7ZgrXvOG6dPJgkP7EiSfEgasgERqTMYvPM9Xz//XKuGNiFmOAwzmhrcAum27BBpKxdxXsfvUJt4Vi6yNup3h9vHw/qj61j4RYdEb2v5toRIeiLN7LnmAHfTj0YN3kSg2IvvL3wLIE8L9X3fPcGy/JqqW8cyBWT+hAX4IaLE0T1HkYHj60cycrBXCu+jw58aFpmVEX6vl3sT0unOno4vSeMYWzPKJr9ZFzxjelOn2FD6Dz7nyxftoCZ3a9gVI/+nJYmOevxCuvNlLt7s+BPn/FObhn+9/YSfWcI7EWSy1Le/vog9ZHXccuUfnSwpjLzSB219dEMm3Qzo3tG4G041e+sDFa88xULthykoNNofnvtFQzpaGpZzY89UCTpxyKo2isEFAIKAYWAQuCiQcCRb2Tz2Tl7DnPnbiaj1bq8ie01kqumjCHJu9WNVif1FbnsW/QR7yw+BrjjEziCO1+4msRWtYCmRtg7neff+oy1OVai77mWZ5u0V6eWmgdnv8I78zfgOuAOnr3CwO73v2HpVzlEXvMI1/72OW7wqiBryxIWbSsm5IqHmBJfxYr5C5i6K4Sx14xj1NX9GX51c3cB8VH4X0CjQ8zAiUyekkiYEY65r2Pqwu1keHRmyITrub6PC0vT5rFi4z4Ckodx5Q030j8c2OvG9k1rWVoWRUhoKEHGFmjaP7DkcXjbSuYsPIgudiBX3vcHfjO+K4FS2+pHaPeh9PFazNqZ01g/6SkGyNf5jpaiTSxYmYPZEsG4Wy+no59Xa6uYRwKdE434eoNXVBgmby9NIkS6z17/OauKAknsMYKeJ1l+9AQk9KH/kH4sXPIVOUeyCIqTB1VDXtoq5pbmYx70GK/ccx2D4ypZ9/67pHkHEzboaq7pdB4I0oFZ/O2T5eSV1pxAott1PHbdIDqFONB/1UFWzZrLvFWplJ7ogbDulzH2msn0DbO7WJ7BjpUzeXfuPruL9oce+IWM4I7nrjo9hS1czifvbyS71kq/u//Cg3eMIsnbTcPbLWkEwxI+ZOuyGay+oS9RgTH4Omq+rDrKvl1ppB3V02VcXwYNSDxOkI7P0dWfwPBoOkTD1hojkRFhuB2/VZ23n62rviMj7j5u7e1ntyhnXD1CSL7qJi57cw5fbNlN+h1CkuR3QA6L/vM/vjfdzSsP/JYpV3eHJY+yt95CbZ/fcn2vMPzcT0WQpANP6vLWsPSHeWxZmIUpKIq4iAGEn6efc0WSmh+T+l8hoBBQCCgEFAK/egTyt3/P3IXTmTFjM1vk29lWiHgQ0mEN6/buZtzo8dw0Lgl7rlSdf4ANM99k2sYSsrYvZt4Oiar3JTTei3HtkKSmpkZSf3iH9XmNNDbFcMW4nvi7ubTk7SvZ8DH/+vcsMlNu4eEHHuXmIR7sLD/E/vcXU0E4vUdfR+eSL3jruxKChk/mhgfuYnxsIeF12WRPP4RHRFeGTZxCj4BWi7ikT8qzdrFhyXTW18TR+dpbufu64wRJVq3zIjC6N9eMimXmtJm8/uXlJD7SD0e/dy/eMp+V2RZqetzO70YmEuDV9s3bk6SrH+Op6OsxJI2nd6S3RqKEIH30n38w45AP3a+r5bGbx5xMlPQhdL3iNp4w9sXs7kz+rvmspIzcI9kcK3AjYmg6K5d8S1ZsKCVrttIQEMeAMWPpYAvQqqugaOsMnp26A71XZ659/HcMdoAAHpz9Vz79Zj7vzdlNaZWd4kTMHvKP3ssTD15JzxhTCxFo++HJ2zadH2Z+xZffbWbr3mNU2VUwxW5gd04N99x6HaMSj7PbmiLSt8zjgw8W2dW0PzQR0cmbsWcgSfu+f57v99VT3/cu/vzIcBI9mwmS1pNXFyZeO5D/PreGD/+7hOTY6xkQ5dOa0NoPaXdclb6HXfsPcNS/BzcPG8aIDm1jrd0I6jSCG598i36NHUgZHNXs1pqbyrLP/sLr366nIsmM0+//wC32RMnZDY/wIdz7r//QqziQnvFHWaolmNnHws+c6X8HpK76hM+87yBu9jKya2pJufIauoV502JEOrKI175azpE8L4bceguX94jG3y2AHmMvI2XZbvav3cz6rbsZPzyF8Jjzw5IUSbL7cKhDhcC5INB4dB3WnF3oovriHNnzXLpQbRQCCgGFwAVHQAjSl+/+nQ9mbuVAYSQ9R93JXWNtL9mlHF63jPnT1jDnw6PsPVJAo9+j3D9AzB1AXSm5u+bzwZvfsMZ7JJNGDqX7ju/YecpVNdLUmMoPX+3VDEpEj2JsTxNu4i8ElGz8hBdffo21Xjfwu/sf4Zahsfi56+hz1TCS5mRTGhGIjzWNpV/N4LDXYK66627G94rC5BbI4H7dmbfjIPsXT2VmcCD6SSNJ+qUSJX9ffPRuuJ4PxcGyo+xYPofPFhbi1/lmbr5zIt2C7B+ADnf/BAZMvpsrNz3Kgk9e5aUOL/Kv8R3sK53i+ADz3l9GZnUt3SdcS/Sx73n16zSKKkUIpJ2y498sO365cO8Ccj170LtjFRnLFzIvvgPRk5K1mBQK97J40ULmbck60UltEUd2baewqpjarAOkurgR7JdNjm930mbPIvNICQ1NBvZN+yePrT3erL6a8swtfFMQxqjKxfzTvxudHx/YPMaJnlsfHZzDv954l6/WFlGddAN/uqYnoT560pe+wber1jHrs2q8o8N4+qahJPjb7CWtuyg9uIb5qw/inHIDf7krEg9qyNqymMXzl7P76C7mf/oZvj5xdH18KPYGJXGBC4rrw/jbrqFbKx5iwGhKIb71MCfOxPq6fzp/fXc1WQ19+M1fHmNkggetQ3aMJEx4iJtXbeLNZe/wj44hvHbHZXQKOpPbXT57Vm1g65Z0/JJHkRRoYdMXz7N8n7197MRU4BBbVy3QLtQUZ5B1dB/uPX9DQtEKPvqgJwN7TySutoKiHTP5+zfbTzRsSmXPmu2k0wRHt7It15+Oq74jJ3EMeS+/wPyDh6m0WKla8wkvFMzA08bFczbzv3xv4vOOkNbgg5/pJoZ3NBHYrR9dQiIwsZ5923ez/2g+/WNiOB80SZGkE49NHSkEzgmBxtzdWHd9j5PBW5Gkc0JQNVIIKATaQ0CUFEW9s7y8XNvKysqoqKjAy8uLPn36ILn4zlupPsyG2V/xTQtBuoWHnrmDqwZ3OG5pqCBreDJhTfDx9DUcXvMD079OZkTX2+gsX5I76TCGd2XsvS8xLjGZ3oGbyXn1NCSpqZGmoi2s3Y28KkFKf3qaXLSYCjnVefjT8bK76Na9Ox6rvuHfSyvQFI0r0zhUUI7b4aVM/c9sdi5OozzUxKr/vcXBec1omDM3cbCwjmMZK/i6vIJymrhn0uW/KKJUVpJLQ30teHvh6eqCK+3qmp/F4y0jY/tCvvtoNkdcu3LTjb9lUvdWDKm5LzdvgnqM4cF7x5L24Hw+fNkHH6cneHrcKV/Lm9sdXMSnqzOprrVSs+ELFof0x90URpjxFCRJa5XDpqmzWZHty3XP38n9Q4xk7c7DNdb7REC8iwGjXyBhYVatRdnhdaxcNJO1R5uHpSadophkBgz9A5O66Fj39/nMqQ4gPOUyhvcOw4daKvK3M+eN79jvH86I597iz35b2KL3bHH1O95Tm10Jm75+gzlbK6iuS+G6Bx7iocl9iTC6kZOUw5GDn7A4bSfzfljHNUOTifEPbteaFNT9Km57eBg+CQPo3yUEAxbyVvvTVHCE3YszMOfsYfem5ezMHUpYK9EQPX5hPRh/16Ncd/x7hjYTbOe0kcbGVL5/5kVm7mqk561P8ccrEnB3bccdLawftz90C6nb3mL2x6/yrKuO528ZSkLwadwH8/eydssOtmVW4WzcxO5dYThHBhMWdjq6UUVe6npWTt+Jbsw9vPLEw8RlL2fVsTA0yQ5nHS4eJsLCmimita6afTOf44stx5eXm4v8i4h7kWf+cB3Vz1/PfWsbqA29guH9YonxNqAjnaX//pa12cVEP/ox96c0Uu0aQ6inrtnq7JlA56QAgldC5t4d7ElLJ29ADLFnDIZrB+I2lxRJagOIOlUIKAQUAgoBhcBPjUBjYyMWi0UjP0J8ZBMy1HZfWVmJbZN7VVVVREVF8fHHH+Picv7+hBfHJiyTAAAgAElEQVTsmseyNZtIKwTfTgOY8Nsb7QiSoOFNZMoorrrlIHu2reHwkUzSt69gWdq1dO5tBFdvgpNGcnvSSKCMzG2bTwthU2MjRRtXs03CEoBeg/pi0h1/6QF8kq/m3mTpIos1afs55uTc/JLq249JD/Y73ncllpjdLN48m/Vuk7liQDcivcHXdxTXGL1YULOarWXVlFSZae898rQTdOhmIw11eaz/5B0WZkcz7tG7tRgfR6hrRXkhDQ11BPv5YHATK8WPIUkW8lNXMeOzT1mS48mwO+7k9okpnEoEz80YTI8Jj/HY+mKe+vJrXv6/OqzbRjL2/tvo267vXQmbpk5jp8WiCcin7joET/yNO/uHQep0Pt/px2Wj+5IYbGzt0lW5klfWrWdDVjUHVmzmYO8pjLyxb+sgf794+o+TDSw5u5j3+QH2x/dgYKiVtJ054O9DrF89teZarDp/TFFeRJk7kBgyhCmP30PXhhIyN77P+lfn4G6IZeSggQxIGciZEzMcZfnsXdRU10HYIG4YnojJ2GwtChswkaGd57Mto5jsbevZeeQaBnYOJqQdY5J/p8u4upP9h8VASIfOdE7qiN/iDEqppa7eTI1duJN9bcePm2hsyGftB8/x9xk7iRj3KE89MYZEvcspyWB4/zt44L58cl/+hlnv/QunAzuYfPfNDO0mFte2I1dzdMMatuzZ3+xim11IkVMIg6+/mUTLTtZsOUipqSfjB8e1ltWvy2LLjCrWf72NzKxjbFmxn6R7buAuW/eunvgmX8njyVfSWGcma8X7/GlmLGNvTmTvVwvJjOxNH7bQYC7FXG/Ey99KRK8UvLOTGXf3A4yKMmEom0PuF3PZlO1K7y7d6DexN7GtYvOC6JAcTWCYJ+zay5H0Y+SVQWyIbRLnvj9/v2HPfQ6qpUJAIaAQUAgoBC4ZBKxWK2azWSM8NgLUdi/Ex3bNdix7aScEymYlcnNzw8fHB6PRSEhICN7e3sTExLTcPz+gVZO+awdpRzOpBJK6JZLUxWZBsh/Bm8j4zqT0j+GrI+nkZx9k06YD3Na7F63eWeybnOK4qdHKoQ2LEWkHiGRkvzhcXNr5RpxIBt91P4Pb7SeXHyq3sGvPIUz9r+K2h27C5v1XuPINcg9vZ2t1Nzp2TqKt4FV5+mbWLZ/LxtbKFO2OcuqLjTTU5rPxyw9ZlhvF1vJs+l5+E0+P74ir7nRUqZzy0gYa6iDAx4jB9ce9islaFnz2BnNS6+n+u79x95Tx9DzlC2ID5pJ0NkyfxV5nV8IT42jaMJUXd65nc+ERevu7oOs1hWfsXfAOL+Bf0/KITwilck8O9SGD6R7ngbsb1FSmMu/DLWTwJI9d04sIb7u15GVztKqSkpJ8ikorqa53bok3a4tp6aE1LP76Dd5eWo5/j2uZ7LmPtw424T9sHFcG7WbbyreYUXINlvwOjB/XmfxlR8mqgK4utdRkprMXPW4uXYiPadvzKc5Lj3Ag20q9GLC6xBJlcGu2fEh1YxLdkoz4rIXswkxyi6qoFoPZScTiFH03NdHUdNxCihcGvR9epzHgnKKXVpcb6y2kzn6ev//zO5xveZan73+A8QkutPsjo7UsZ/fMWWzIyceQkEDkli3M/GAPB3MPMbBrJKZOQ7huZF+Sw4//5BbsZsGyPRyr0hMS4k2eWzwR0fFEmsDpaD5pq2YwvywDn7DfMTrObjHVlZTnHuOQpZrGvGPkWXR2ypQnltBQU8aeWS/y9qffM8/9Rubd7cufv1pMVsQo7rtjIN+9+DpL5sWRs9CXMY8OYOszGRwrqqcuHAxZ6aTW1lJNItHhXni0Y9gKDo3F6C0MP4vcggJKyi0Q8uNNSXaf5hOLUUcKAYWAQkAhoBBQCJyMgLi/VVdXa9YdITn2BMf+2EaAZC8WIrlXU1ODuNDJJiTIYDBo5MdGgDp27KiRICFC9puQJPtzqe/s3B6hOHm+jl0pID+rnFJNpcGHYP9gAk3tvImIPckngKAQeRNNx1xRRU5WrhasftYkqamJkqJmigQRBPg5cUrvwYo9zPl6GfsLK7ALrQcqSVt3lPwaqFg/k884wtLjEzGnr2dneiXNsm4noyDPoNFqxdrs5XVyBQevOLkE0f/2p+iv1bfS2Kg5D56hdRYZ+y2Yq4PoHh6Ap4ct6OIMzdq5XZ6+ifmf/YvpW/MxpoyjY91B1n7zBrZQnZObWDGXHGLtN1+yrtJE4rjfcd/oEWxb+A5z35uN+0Q3Zu+J58HxHWjWJzvCopffY3fkLTzYfTmHDuW3UnDzSR7OYI8v+N+n8xjcMxb/rkEtsSBVucfIrSrHbEpm5JRxDO9lk5K2n1UNObvWMPvjV5izrxz/YQ9y++WhOK/ZBwYfwrpfwS0Tx5M09TOW7i3Eb8LdjO2wly/m7iRDwpgiG6gvLyHPRU94eEdiTqtFbTfu0UPsbmhott9JXJhOZ2eR8cbb1wVX7bEUUVFZQ60Y+uy4gV1PbQ5ryD20j3170yiTOwEhBHboiD2vaG5goThrEzPefIFU2w+Ph4mgPhO4Z0hkqz6tdWb2//AS/3j5I4oG/Y7RUTrylr7HP5a2qtbmpJxd3/+XxXsK0Pe/jRvuGoll79fMXbmSHWXepB9oIjw2nsRwI24UsGf+bDZk+tBp4HC89y1jnijEHy+G0A50jvbmhyXzmTV3ED0e6o/NkbOuqpLCnCwKDQF06zuGm28fSIStoba3YqnMYu3nb/Px1Pc5kvgnnpg0kT6muWg/8EGdGH/7GDo7wfwFi8kZ+TBTxtdT+OJCMjJqqUsCykvItzZQFxRHRKA77u0wF6+QcEKMvniSRVZeAYWl5XBm8flWM23vpJ2h2qumrikEFAIKAYWAQuDSRqC2tlZzZxNCI5u4ttmO7fc24mOLFZJ70lYsQLIJgXF3d9dih9ojQEJ6bJuQH/tjT0/P82wlcuSZVVNZasEsZiT88PTwbvfbWkd6cqyOEMUySkX8TisB+Pk54Xwq40vmCt5//SWWlEQy+so+dAiyuXVVU1NnxdoE1joL5qoqqo73YTZbqLc2ERpgIsRkL0fcPKJvbF/G39WX8cdn8LPuKrI4ml1LTW0C3ZLC8fM5F5JkIW/fRuZ9+iozt2cTNOopJvUNo2LzArYXy2qqKTi0nZUz1pJtCidpyNWM6tT8Nl5rccVdxAKqDXjEXMa9z/Yhp7eRrnt7csMUJxJWB7fISJRt+4KXlrkz5a3bGH54A25tublPMj27evL5p9P5aEE/OoWOoPNxnfW87CNUVZThl9yL5IQoLVdTa5yFIC3m2zffY9FhSLrxaW675grC8ufy6a5USg0B6H38iY6NpmbUBLJqixl51+V47M3H15DBpj3Z3BFUw7GMNDC44dI1DkcNSZQWk91opaH1hNo5K6C8soY6R70hSw+zbvUKFm8TBudFeNf+DBs76IQKX8sINRRlrOPrl9e1XMHTn5CBh7DeMJEr7xpMJI3U1+SyceoHfPjxv9jf4c889czDBC1/jR8ybJaqdJa9OZMdNbX4D7qJib3DMBpE5l+Hm6cTzi7QaOzCuHvuJKE4lm6rnUjo6cuRSh86h3hqrq7mjHV8vzoD1y5juaZrLnPSbZIbx6dmCCU8JoaQplksnT+LXiPiubOrJipPdVUZudmH0ftHEN9/YJvUAFYs5UdZ/flrvPnuUpzH/pln7n+SMZE5bHhzBRoPC/bH5ORP0B23snTxdP74xJUkR6aSFFbAvN1HqR4eiiUjjdpaM3SKJtzTvYWEnwBOrH8++OsNGo8tyC2koKSMWoJPWAdbVXb8RJEkx7FSNRUCCgGFgELgIkVArDg20iN722YjQ3JuT35EJMFmARLrkbjQySYEyMPDQ7MAiYCCuMDZLEA2suPr69tCfOSanAsZEuKkiiAgvkjFFOXb0AgmwHRqS1L2rg2kmyup7TSWe/74AKOSgo7HvuTyw1/SOHwwHdOwKdz/8Al3u+rDq/hh4XLyTEMY3NEBLWjbVH6Gfc76xewqL6EqfjSdwnzwbsORKqvN1Gpv5afz72qkwVKFk29HBt94OyPHT6SXuNgNH85kWUO9xIr8mwMztlAT0ZcbHn+RJwYJM7JSmr6ejw59yppSA9379STQ3ZvAyS/Q/fja/y7f3mslncX/WI7rzU9z+9BwrJltGZJU8iWpeye8vGcz77NF3DS8GzH+EbiTz4Fd6RTnu5E8JpmE6MB2vtdvor7GijG6P1ePvpxR4/6fvfMAj6pK+/gvfSaT3jsJIRB6aKEjTRQUAQEV7H3RVdTVXXV1XbuuK+pnb2tBUBEVQelFOoQWaoCQTnqfTDKZmu85N5lkEhIIqBDk3Oe5Oefe+572v5OZ+79vG0Zn52w2L93Iym2pVPtGoamqIDenip1bkjihmsAdXu7oPMPx8y9n2epNHE8M58Dugzi7RhEaF92g/WqY/vkuavM5vH4p3y/djggIp4noxfgJk5mcYGf7qAmmy7BreeSRXk2zM+sozdjN8mV7KVjzIU8ezeZk92heHB5GndVErU5H8GV/Y8rtLzBNJBuLf4XhttaV33H7J8s5qPdh5B1P8Mz13QnSCJJUxcrHVrLjcCEhCT0J9/Gic/yd/LWxoa2DEpJ+3kWBewKXXTOWPobvWWa71FiqCQqLolOsM19s3cSKJeO4ptd4AqmmvCiTY8laAoI6M2R4j+Y5lajX1prNnvS94QkmP3Abg32N6E8m8cV7K8gJjSCspJzKugpyvvsf6+qGcLOfE86OMcT2dubI6g2k3RaDdncyNboaAmIiCXRXN/d7s83RXYOnq2v9Z0xbRXVNLcINTAkeYZM5h1KSpHMATTa5NBGoqymjrjIXB48gHDwCwaG1H4wmbOp0JdRVFeCgCcDBy+5LsklE1iQCEoHfiIDQ3NgIkI34iFKYxNmORV0QHkF87Hdx3Ww2KxHkROni4oLQ5AjyI0pBgLp06aIQHkF0bLsgPr6+vsqxqAsCJHyHLt5NjcbLDZUwJ6o+/SqMRgP6moZsMK4uuHholDwpp2/1W67msXnlbrRl1XS+pjuhXi0SmLbRtSZ2FNffl0j+we0kLXubnf59GDhoML1Cf7ufQhtDtu+07hg/L1jBwUJfBj02nv5R/qdYcWUmLefbT8s5EOyGd00qJ4pqW+nbnYj+V3F7/zZ0YTodlfm5pOGNn3cPEnra4kybsZgrKCsGZycnAlvRsjUNpoeIaTxx9ygiVM7YBeluEgGiE4YT4bmZI4fWsWbvNIZ0CyHW8RiHD5dRZOnNZb3jiApsDXd3gjt1Iz6hkn2Ze/jlf3ugNpcDGzdzsNwJJ6c80tbM578ZWnb+vI3iPk4MTO7ODL8w4qM9mb95CauSx5B81Bl3dTgj+nZqNq/THnj5EOToiODpp7e49MNDo2owvTtdjxVkbFvKp+8sYFVSNgTE0n/KLG6ePoqutlxOorlnBL2uvJfXr7Try1RJwb4f8a98gXmb0qio2M7XX+/jH8Mj8NJEM/6h1xlvJ96smn6CgxYLJvowpH8wakWLJCTKKS+p93vz9/HGtd52sFnT+gMj+CVw1aRu9O0dhpst6lwLSb+waDrH9sT7x/2k7drMluxhTAstpeTkMQ5lBBB09WCGN37GbI2dUbmH0XtYf07sKiJpwdsk1Zkw5G5kjfAFjHbBbd8PvPnuUY6+8xFbzCd5+9cBPHNFN/oMjMdtyWp+3RdP9T491TUBDO4Vg49na58jgasn3ipV61om23TOoZQk6RxAk00uTQTqilOxpKzEIagbTt3GK+RHIOEQ3AOnngYcA+MaganTV2LN2II1Zw+OnUfgJElSIzayIhFoLwKCuIhABoLk2HYbAbI/FgSovLxc2W0kSFw3Go2NBMjNzU0hPoL82AiQMIWzER8b6bGV4ryoCxknJ/Fm9s+8BREc5Y2vcDTIqKKsopTySgP4nfoeVltZSmFBtgKGytuD4KjQFm+Pf1+c8nf/j2VbSyivhi5FyfyyqIKkxnQvlRzcl0tJLeiT17Fkvpb9zXxSqknb9BPLV24mP3QGf33Kn4hZvZpHV/t9p3uG3nQcX/kV3/1qImzYXTx6wwhiGnPXaOg0+Gpmmrtwsloo2tI4UgoBjuVo9ad/jD91UBPawixS9h6g3CeAfv0S6dYMl1NbtH6mOzPndW/9kv3ZyGjiVSq2cYjVW1O4Y2w/YjlJhk5HXe9R9O4aSVAbz7YWfTkF2cc5ouTi0ZJ7MJndWzNx6tyby0b0w093gk0/rmVvZTBR1hrKy/S4xwXTY9hQvL5dxOL3y8kt8MBz4Egu799qaD77mTbVA4KIcnQiRZCkiip0VqtClur/06uprjJjVmzx4ogI9cbztP5IwvRxLd98+Ck//HqEMk048eNu4s7bZjKuGUNqGr5ZzcWbEBEZ8vaNfLEpjVKzGUtRKWVKTMlmki0OtBxZt5E0owm/QWPpF6rG7ay/qsJInDWzsd9GC9jGMw0VHz8CA4PpRCFp2XvZvKuQaWNrqMjP42RgCAMGD+MUjiR0xhYjuqI0jhwRFNuCUZ/B1i/WkeMcxvAhE+jpVcfeD19k1Qkrlq61lBTqsFrqiBs3jR4v/Yt1H3xBaXI5Ws8RXNa/E36eLdSutnmK3wmj8TfFiLR1ZV9KkmSPhqxLBE6LgAOKdqjiJA7eYThFDwEnV5xiRyp7Y1OrBWthCpbUDdQZdK1GemmUlRWJwCWKgCAwNgJkK23Ex3YsCJE9+RF1QYJsBEj0ITRJNgIkzODsCZAgOW3tggQJjZEtitwlehsalu1JVEw0QaG+kFHKsZTDHD6ez5iY6ObmKsYyctOPcihFRHjwIjyoG0P7NEv+0k4YHcDBC+9GV6FytFV11AlrxBZ+SVW5tURcPZXrFQ1XCVlHSsgqPsjKrYfJVUfQlSJqzGAtSCdlvwOl9g+ztQWk7Esjq1yDT6gbznUmWtPJtHPSv1ksY+tC3j2wGmOfa7jvmdu4LCbQTovkTZ9pc+kzrcUwhxfwYM4hJZ9UiyttH5rKyTyaxOp1afjHXMXIGaOIaZS2YDHrqCpzxtHRHy9b0IDG6+dQ8fUjwKU+OlytwYjFaqEk7SCZ5UZixvaha2Rrpnb142hihjPj/uHMEEHQS5NYPO8Fsk64ET/7Mf710Dgctn/Mi0c3k+E7lOl3P8DcscLrqJrAHsMY4fkB769Zg7NPFP1HjGPA2VhVeoYTFeSEcykYUjLIrjUiDOAUg9jqoxw+qkMrfPR8oggL8kBz6vuCRqAqM7ex7KN3WLB2F1mCII2ezdy7hX/QWfxvODji6OqmaGVLnZxw9PY8c8TIqmS+XLAFg8GHsfdOpo+n2i4AXzVVZRbMZj88NS785mwBajUaDy/lBYPJbKXWZEJfVkT2iVTMwbEMHtaj1ZcPThp/4q95kvevgTqzjpM7XmH2V1uoGX4nz775b8a6HebVaZ+w5mgQ/e96jjdvGoCX0IZ1HceU+Fd4Zv1KSoSya9IYBnTyPcU0tfEmVGkpr62tV4Rr1KjcXO2waJQ664okSWcNmWxwqSIgNEgOwfFYU9djzUrC0S8aB9+oU+Coqy7BenIvdZV5OMYMwzG83yky8oRE4M+KgIgaJoIYCKLTchfkxnZO+P/YCFBZWZlSFxohcV20F7voS0SAE+THtgcHByvmb4L8+Pn5KbuNCIljW136/5zdJyys9yj6917PuuSNFBzbzeoVa+jXeSrD4wLriZKxjOy9K/jh2xVsPgZu/nH0GzaZCX1tZlxnM57wPwokqD6/JLCf1DQr1gDF37xZR12nvMCrU+xOFR9m9cI32Ln/BHWjrmWibh0bNyXhd+XdPGHnk6S0yF3Nf596nqNHixhw5ZVcdU0/LqTh864vPkc/7hYe/fffmdo3hHOK12AHRVtVfVE6R3auY482hLBxo7m8l50ayVJLbVk2admuOEdHEXw2xKKtAdHg4eOEU+cBTBneiyg/B1KW7KfUGMbwvl2JbNXUrmVnRopSc8nNq0DdO4A6Yz57vv+QwxuXsS2/E4NvnMpNI21hGVSoNCFECOONLFBr3EkYOoDWl1JHnTaFVT/tobiujoABkxkf74NLQCJXjgrl24xKqrN28suuDIaFeqN2d6F4zzI2Hi6kRA/hY/vRJSQAj7LjbNp1mMzCKrxiBjCgd1cifVwwFKawbsF7fLJkI4fL/IkZfS1/eeRubhwb1wrJMaEtSCP1eA7EjGBAZIOPoqWGipwDbFi5RzFpdFWriE3o2cZ6mnDL2/gFq44bMUbPYObQCDT2aqTqLNKzDOhrI/D3U+P2m62BXRHzUkeE0b3fYCb09KekaAdHjxcREjOLYT3tPmNNU7SriSAU5RxccQyXgYF4dvLi5Mov+bJwDZ9ttOLb70b+Ob0v7q42VVgEneKdcd1Zn0KsR2ICEd6nMbXV11BlMiovQTxDgwj08/ldzIAlSbK7hbIqETgdAg5qb5w6j6CuLANrbjLWkO44Cd8kFztnbIsRa95BrCf3gWcQjlGJOHjagmWernd57c+OgNB4HDlyhNzcXBITExUzr4tNiyHWIBKgCiIj/IDsS/u6IDuC+JSWliqlqAtSJNqI9mIXaxdERuz2BEhoeGzkp7VSXL+4/X866Cc9vD8Tp0zk2IF0vtuezOqv3oOafGaMiUNxqajJYvfKJSxYvFPx4/AM9CWoe4xdwlID2sJ0ktcnk0sNpZl7G3xYjOh1h1n39dfkO7rgGpLAFZd1wd3BAU8vwYqEgc8JUtKqsAxSwWnyC9VkJ7H5+/d45M3dOHcewj1TR+C/dhcbGyGtIDMpmSNaT7r0jickJ52sghzK3CPxCwrC3943pLHN+atEJc7i4VeeYFpCEPbphH7XGehLSU1az6K1B7D0n8akG69iYKPGDjCbMJWVUoAjjg5eaOw1b+c8kQgGXHs10w3DuWtaIrGaDH49Xok5ahh948LbNLVrPpwJs0cXBl9xB4GF21iw8B0e+88xJbGpR6e+uKjMFOzZx8lh/Qgy55O6exV7sgKIjDRTpq+mMnUHB4quok/Ln1th2XF8EQ/d+hzH6+qIfWore/45GBenQBJnzGTA+nlsSN3JZ299RLfSYUT5qUhduoCkvCL0YX2ZOXM8AzoFUrPzHf771Acs211EyLRneeup+4ns78HJ7d+yaMl6kpR8W96E+rpiKdzNz1/bOfeoAojs2puBPTUUpqxh/ssfc6LPHG4c0EAsTOXk7vuFt+fvxlntSWziFdwztlmG2uZQiaP83bz/3kJSPHsw7sG7uCLanUZ3JHG9tJRCswkjIq+Q8O86tYuzO+NDeK+hXPVgAC6DZzO1u4X9yzLIqg4j+sohnJEjUUedkwPOsbfw8uBuzPvqJ567dTNpDZOIiw+lfMtGUr3H0tXXhdID37I22Rm/4ABKDOUYM3dxKCeBMC9/PFohfLUFJzlZVYkW6BUcqCRpPrv1tS4tSVLruPxhZ8WbUb3JQq3JglXJlQFuzk6oXZxxaiP+qdFixWiul3d0cMDV2QkXp9aTsokEZiZrc3nRv5BvbRMZHUxmCwazVXyEEf2fbi6t9XEpnXMM6aFohiwVP2PJ3IGDf2ccg8WXWb2NSF1FLtbsXaCvwKn7lTiGKSnjLyWI5FpbQUCYje3atYtPPvmEnJwcPvjgA4UktSJ6wU6JyG2CxJxuF0RIaH8E+bERIFGKCHHimtiFBsjZ2VnRANlIkChFEASh5fH391dIkH1pq4sACKKt3C4EAl50n3gjtxc74eX1A78cSWH1x8+w+uOmuai8vNAEBOBRUkJVXjrbl37FAvfBRAVG0ysxjKojS3l99uMsbWoC1FBRuIQXZi8BVy98x73G7su6EOPoSJcBY/DnO0TE6gOp6VisQhfQytNcTTa7txxi/8p5vJtkIbTXLO576VbG93Vi0y77wSrJ2rCQV1dV0mXUaProVrDnaBbunQcTHhN2xjfz9j39EfWI/l2xHN3E6syz6D0niRP54tGvHZu+lOykJXz+7Uq2MYnb776Ph67s3KyhqVZPYeYxKl3UuEX3pWd9JOdmMmd/4M+Qu15uyBUFtYd2siOlhsChfeka0WBqV5nF3oPHSC9wpXPfHnSLC7IzNRQjaoju1ZvwCF8CjzqTe6wUw0lnuvbvToC5iOzvn+beX8dyz/N/Y0zhUj7+5Cv2Bs3grxPrWDb/W5a9dg8my4fcOXkwk3rbLUrkJSvI5bjyhANlVdWKhlqMGDhmDo/dmYvL/K9Zt+cTHt/xSePSQ3uP5orpD3Lr2G5EeUF6WRG1+vqoJjp9LQaTyC6r5WRmJkV55Q3t0tn24+ts+7Gxm/pK8BCm/fVZ3u85Eo/ALkSHebJmwX3Mft1Oztkdr+hEJl8+hFE3Ps11bbqC1UHBPn547yle2xvPyFue5p27EvFukWurJOsYJbV6zGE96RrmiWcrxMJu9HZU3YkaNJ0HBtWLmov2kXHwINl10Vw7tGd9VEFjFSWZB/n1QBkevl0YOC4e8RqkfnPCzT2Cy28NpvKEimlTTJR9fxSPCZfRRQPVR17j7m98ue6rr3ksKIfl7/6Dz4vG8fAdMRxe8wMrv32aVwU3nH0t1w2JJrDFggrzs6jSCi8uD0KDgvDzaXRctE3gnEr5a3ROsJ1bI73RTLneSFGVnopqAyaLyKfhgI/alQAPFf4eKtxdnRWiIkYwmC1U1ZoorzGg1ZswW604OzrgpXbF190NTzcX3Fzqf1AEORLy2lojFTVGpTRbrAo58la74qdxw8PNRSFYom+bfKXeqPRfVSvsiOtwcXJSZEUbsbdF3M4NgT9BKycXHKOHYC1OpS7/ANac3Yp/koPKG0x6xczOWnBYCe4g5BxUXn+CRcsl/BYEhOZo3bp1vP3220oQgenTpxMYGHjefGHEixkRAEFob2yanNZKYQpnIz/2pSBANjM5QYCEFkeYwAniYytDQ0MbiY8gPGIPCAhQStux8DCvMzAAACAASURBVBX6fROg/pa7Itu2jkAEQ255lCHjRtDv20Us2pTeTMwnMpIQLxcyVixl/b409n3/End/70eP0XfwwoKnSQyIY/CUKS3dipr6cFbj0aeT8nDs4OhEyMjxDOI7VgGpR9PQWQbggZNd+1KOrt9DRsZKXn9zG6YgH4bd9m8eFtoKf/HUl9/Yt77WgMkczmU3zGaWcSFfLXqZpSdysAT1YOxVE7isb6dWzJ8am5+XStaOpXyf44HqbJ68tCc5drICrAaMtW07ppu1BWTsWMz7839gUV4cf3lwDnNvHoivMOXKO8G+vRnKW/aa4hNs/PonTvp0ZvjUMfQ8l5VXHGP76uVU+mtobSllO5eRdLKWIF02ydtWUSSCNmRt4L0vvmf1Xk+uuPN+Hn58JiO62BGlmiLSjh8jaePPrFm3jh2lXZj0138xe8519NAf5NdF83jp5xMsfPnfHKo8SHq3mcy47n4eHu/FmGArc19bxA8v3MKmvX/jk5v74uwaTeLEXgRSR3W1kgBMyQF2Wb9YnJ1tL42DGPvoRwSHOvLG8gJK7CI7DrrtJW4b14PwBp+tzgOncPk1Vty7FBI6aiDdQsVvu4mA2H4Mv6ICL/F83tbm3Y3ErkG4ocan10Rue8aP0M4v8/VeuwaqQCKH3cy/HhzVVv5jqLOiS9vMuvkvc92CSibe9DRvPXcN0SpnHGuySNp8hCLxLAekfPsle4pL8Z05gt5BfvXaYLvhzljVF5KRvIkVPxW2+n+jz01m/cbDFJl6ok3bxE9i/VW5HFn3BU9+fozQ2Ok8/vHfuXFMt/qXExYjtfmHWLH9KFnLX+SlvRGMmHo3c995katCoXD50zz07i+s/ez/cMxdQ0rgIIbddQ933j0c16HBPPHy+3z37dP89chxKm4bR6+wWOKH9CA62As3Ksk4lkFZkRY0A4mKDCfoXKyAWwGltc93K2Ly1G9FQBCktBIt6SVVGEzNI9WUVRvILqsmyk9D50AvhZwYzVZyynVklurQ6o2K1sk2B6Ht8VK5EO3vSYSvBpWLEzVGMyfLq8kqq6JSL95wNG2C6AjCY5N3dXKk2mgmq1SnyFcbmqdTyygBPw834oK8CfPWSKLUBKVScwyIxalTIubyLKxZu8BFg6NvJHXVZViEFglwjByAY2DXFi3l4aWEgCAmR48e5euvv2bhwoVER0czZ84cJk2apAQM+D2wEARI5PCxmbC1VgpCJEzdBPEpKSlpLIUJnC1HkCBBoh8RAEEQH9suiFBYWFgj4RHEx7bbEyEhf7GZDv4e+P9p+wgfwuxHxN7aCks4OrYHsfO+ZlN+Fsf22d6iexHe+1qeXHJta41OPefgiENoHwZFwuqTUHdwL4d00wlQu+DiUMqxDfvI0u/hi3sXkB4TiNdld/Ps369lUJRvqw7ZpeUVVNfoofto/vJ0P4b3jmfh9+uo7X8bs2+YyaCz8KE/dbK/5Yye6so66izeTPjbB7w0rQshZ2PiJgI3PPEyby+rQFtahQi+Hmw3HX1pJunHj5CSvIO1q35hPaP565P387hNg2TWUZy6he8+WkJK+UlStqdQovIgdsRI7hh7ThQJ9CmsW1jBDnsfGLs5iWpEr86Qs5Wfv9zaeMU5pA+TJglum8HR/Sfp08UXU04qJ05kUpSxlyUr17E61crwcdfx75vu4boED6pLMjmemoM1ciRTE6r5cnEBLtMeY96TcxjZoKbod91zvOvowCNvLWfrz2/zdOkwugZPJGxiT/ytOg7sOVCfeTXyKm4YGY54BrLfet74AZ/caH+mlXrslTz20pU81uJSz8lzeX7y3BZnT3/oEzOY6/+1pD6f1elFlatWi5Giw2vZk2Uid9mTPL41kAlz32Tx/YNwsxG+qqOs/upzdpdlcXjTfrJ0tahiBjDn8kGE+52DnamxgGM7lvN1RpMx6ylTVXWmR5ie5O8+IrnxYgCTJokbU0Py4t2MHOJH6d5dnCiuomzH/7hzUQUT+nbnrhff4aWrhYdgNZk7NnHUYSi3TC8n7V+byep+LQ+9+TzXxvvgLvQAox/mZUCl/pKF65bx8YfH6RwynrvDQwkWJEm7l51JeWTlgfuA/vTqHkPY2fyPNc791IokSadi8rufERqgtGItJ4q1ivaotQGETEapTsl+HBPgSXFVLeklWloSGNFWmOlV6I0cL6rEUldHiJea3IoaZQyhTWq5CQ2RIGLCxE88VAV7uZNZouV4kbYZ+bK1EyZ4pToDutpSHKIcCPdxlw9ANnAaSseoQTgWHsWStgnr9o+VNzzCN8nB0RnHTokKScKpjVCVLfqSh38+BAT52L17N/PmzePw4cOMHDmSRx99lB49erTbnEz4/4jobUJ7I8hPa6UwbxP+P4L82Pbi4mLFJE7MwaYFEqZ0gsgIEmQjQsIPKDIyspH0CO2WjQDZSkGERO4guUkEmhAIIH7cXbw0bhqpm5byvxd+pqRvHEGnif7V1Na+5oij40Amz+nGe08fp+zoClYe+AfDR7vh4pzDlk8/ZFlRJTXdr+fJD+9lXExAK47YrgR0GcCQ0Q7U9ooiQEmiKcbwpvfUR3h5aqssz34S56FuxD10BCNHBzKyuxfuZ2v2pAmlW8JQLq91p2e0/yl5YLTZ+1i/6H2WHQaf+Cn865YHuMneCcktiLix9/He2JvJT1nNR3M/ZI9/JMPufJmb7fKZtgcIdXgCl42xUJ74EJ/NGUW4TxtxvdvTmSKjJeXQBpYs+IkkETAxZjSPvXEb94+25TuqoTx7L6s//0Ah0nhEMebvc/nnnOF2Zlzg7BVCv9mvsDA8jvs/03H3m/9monj+rrNg0h3kl19SqCOchLn3MTnSBZfmHKnds71QglajntTVb/LWapGeMYpbXviU16eENn+BHXwFT82/Qhiu8uWdr/JTTjGBM/7NfRNiCT0LjuTqE0X3AaO4PD6BG2+8kVuHhv+GZZuoKtzP9wveZOEJkdnVk2v+8Qrf3WsLZiWSSleQvOgtPjxsxVIHvtMe562XptHVU9XM8DZw9MO8ExSKy7xkusy+i5nDuzR+51Tt3UFSfg65uNO/fy+6x4S2MOU89yU41ImnZrn9oQgUafXsyChSzOHONJDQ+gR6qNAZzOgMzTVCrbX1VLmgcXNWzPGENulMmzDnE6QnrbiqVYLUsr27izNj4sMUM8CW1y70cWpqKnPnzlX8IH799dfzPh1r+lbMe7/GWiz+++v/jRz8OuE8YDZOXUaf9/nIAc8PAoJwJCcnM2bMGJ599lkefvjhZgML87pVq1bx6quvKgRD/NDce++9ij+OTdsi+rARIEF+WtsFARL+P4L02HZBhMQ5oRkSu5ARmzCBsydAIrS1TdMTFBSkkB8bCRKl2EUAhD9//p9mt0YedDQE6qyQt5AZfW9jSZmFro+sY91zowhxd7Yzuetok5bzuVgQqDPXUrD2YS6f9BHlvf7Doj0PMdTFSabluFhuYLvmWcWmV27iH+8sZUf1EOa89DKP3jKazlKT1C70OoRQVplO8Sdqz2SEn1JGSRXOTo5tBluw70f4K5XoanF2dGz+VsFeyK4uiNehvHLFN6lFOgo7qaZqjclMfmUNQrslzPzk1oSAYlJXfBxrZR4Yq8FZhVPkIBzD+zYJydolg4AwVztx4gQfffQR8+fPV7Q0Dz30ECNGjFCITUFBQSMxEuZtgvAUFRUpBEiU4tiWCFVoh4T2SPjw2AiQrfT29qZLly4I8mMjPLbSRohEAlTp/3PJfPQu3oWK35Tw0dxw/XBKDlowa/OosFoIwrnZW+SLd4Fy5hcSAavVwol9B/EdMYJbXn2EIc4OkiBdyBvyh4ydR7kunKjo4Tj1u4FJw3v8bgRJTFea2/0hN62pU6FfKK7SK0ERms62XRPmcoL4iOAMbUWks2+tN1oUfyShURJaojNtov8CrZ4oPw+c2kl6ciuq6eQvSNKZej//14UiVEQOO3DgwPkfXOiPtGr8HfzwraumvM6bMq0bpIrM0mLv2JvQZohNPEzbNBwde8YdY3YCN+FrZL8JrZDQZr7wwgts2bKF2NhYJk6cyPHjx9m2bZtChIQGyEaChLyI4iaIjzBnE6Xw/xHR34RJniA9gvC03IUZnJCT98sefVm/eBEQPyoRzHh3o5JM9OJdh5x5R0TAyVXDyCe2sPmJjjg7OaffB4FuTHnhPexTqf0+/db3cuan6t9ztEuwrzqrsLNsv0WjCPUttEntbWK0WKg2mRWTu/bAa61DIWzCT8npNPko7PsSQSfqJ9SxWJJ4yBRv1UVoZWHSdCE24f85O8GTKd01LE05wbfvJ2Mwt/9+X4g528YUjvxC+yHMroS/itzah4AgSYLsiM+eCFktiLowh5s6daoSPU70kpeXxxdffKEEaLARIEFwOnXqhEiGKsiPrbQRIXFdyMpNIiARkAhIBCQCEoELj4AkSX/wPRBvfM8mjLYwaRPy7VTyKG+U6zVC7SMwQkr03T7penCcHB3PrsEfjKmte/FQOWvWrMYHVdv5811WeziwRV+HIQD6DTzfo5/7eELDIYhSXFwc4eG/xTnz3OdwsbYU/9dC6zN6dL3vmQiCMGTIELKzsxXTOhES+/LLL+eyyy6jV69eSmQ7mf/nYr3bct4SAYmAREAicCkiIAM3nIe7vjO9iNzK6naZ3AktkgjPrXZxUpLMnml6IjGtCCmudm2fvNBUldUYCPZsv8mO8EfqFxlwVmTvTPP+012vE6Zrgn1ePGFzHnjgAfbt28czzzyjPND/6e7JeV6Q0CgdOXJE8UdatmyZ4m8kNEfXX3+9YnonkqkKrZ0kS+f5xsjhJAISAYmAREAicA4IXDxPdOewuI7SpFOAJ65OrWQRb2WCIudRhI+7kii2lcunnPJ0cybIS4XapX1KQbWrM10CvdodhEFotkR+JRm04RTom59wcLqoCFLzycuj3wMBoV3q2bMnr7zyChs2bODpp59W/I1ef/11Jk+erPgrJSUlUVhYqESzk4FFfw/UZR8SAYmAREAiIBH4YxCQJOmPwbVZr0GeKoK91GcMxCDM7EK93ekR6ou/h+qMxEQQlyAvNd2CfQhoh7wwy/PXqOgd7oe725lDrIr+xbz9NG7tNv9rtnB5IBG4RBEQfkYPPvigEszh3XffpXfv3ixevJgpU6YgNHirV69WzPJELiS5SQQkAhIBiYBEQCLQ8RCQJOk83BNBNvpF+RPmo0Hl7HQK+RFvoEXI71AvNd1DfJRIcvEhPgSKZFrCP6nFHMWxOC+uxwV5KySpW7A3vu6urZrECXkRIlwQr4RIf8K8NQyIClCIUlsaIiEf4OHGoOjAU+bbYjryUCIgEWgDAZG7aPr06fz0008KSRJ1EWjk5ptvZsmSJUpkxjaaytMSAYmAREAiIBGQCFxABNpno3UBJ/hnGVqQjgFR/uRVqsksqaJCb1R8lASBEeG+o/w0hAsS1WA2JzRDfSL8ySzRkl+pp9Zsqc9X6oBCtEK81EruIh/3+jTnwV7uig9TWkklhVo9RpG6WARZa5AP99UQ4++JCBUutkBPNUNigkktqqSgsgYR9U40EITN1cmRKH9PugZ5KfmU/iz3QK5DInChEBD/VyKwg9gzMzNZu3YtiYmJMqrghbohclyJgERAIiARkAicAQFJks4A0O95WUSJi/T1UPZak0VJMCs0Qm4N2iXBU4Sfgi2AtLfalYTIALoGm9HqjRgtVsVkT5wXOZGEnLUhVrggWz4aNwa4B6E3mamoMSj9u4kw2WpXhK+T2Gx9i4c2YUaXGB2ISBhbXm1QSJubixMBGpWi2fo91y77kghIBOoRiI6O5q677pJwSAQkAhIBiYBEQCLQgRGQJOkC3Zx60tJEXATZqTHWkxuD2YqzowN+7m64u7kggi3YJ4pVyJG1Dp3BRIXeoORVEoEhhFZJ4+Z8irxYoiBfZmsdNnImEtWKCHqODo5oXF2U/QJBIYeVCEgEJAISAYmAREAiIBGQCHQoBCRJ6gC3o1Jv5ERRJbkV1RjNTY7cgiiJwAldg33wV4InCH0RlOpqFfkCrV4hSLYluDk7KoEfYgO9FS2ROC8IVY3RRHapjqwyHVW1JkVc+CIJDVOXQE8i/DwUnyVbP7KUCEgEJAISAYmAREAiIBGQCFzKCEiSdIHvfll1LXuzS6ioMTaawtmmJDQ/uRU16Axmeob6KoEf8iurOZJfQXmNwSbWWAoNVGapDm2tSYlgJ/yOhBndgZOlFOtqG+VERWiuRB97c4zKGINjgqSJXTOE5IFEQCIgEZAISAQkAhIBicClioAkSRfwzlcbTCTnlLZKkOynJTRNxworEIRKJIJtjSDZy5dVGziSX06UwUxOme4UgmQva7HWUVilJymziKGdg5XADfbXZV0iIBGQCEgEJAISAYmAREAicKkhIEnSBbzjOeU6qo3mUzRIrU2ptNqgEKTWrrV2rrzaiK62rD4qXmsCducEURKapgJtDaHeGrsrsioRkAhIBCQCEgGJgERAIiARuPQQkHmSLtA9N1utFFTqMYjQ3u3YRMQ64YtUbTC3QxolqMPJFj5Op2tottSRUaI7nYi8JhGQCEgEJAISAYmAREAiIBG4JBCQJOkC3WYRyU6E9G6I4H3GWehNFnRGsxKm+4zCoPQtfJOEX1N7NuGjJMz55CYRkAhIBCQCEgGJgERAIiARuNQRkCTpAn0CrNamnEXtmYLVWsfZtalTCJgI/d3eTfCps5Fvb79STiIgEZAISAQkAhIBiYBEQCJwMSEgfZIu0N1Suzrh5FAf0rs9UxC5koS2RySfbc8mQnyLkODOju3nwSLkuEgyKzeJgERAIiARkAhIBCQCEgGJwKWMgCRJF+juuzo7EeihQkS4E2Z3Z9pUzk64Ojni2E6SJBLFujipEElj27MJ8hXm494eUSkjEZAISAQkAhIBiYBEQCIgEfhTI9C+J+g/NQQXZnFCXxPhp0Hl2j6e6qN2JcbfEz93t3ZN2Nfdja5BXu3WPAmNU7S/Z7v6lkISAYmAREAiIBGQCEgEJAISgT8zApIkXcC766N2Iz7YBw83l9POQmiFOgd60ifCj5gATzRupydWnm4udAnypluIL7GBnpzJgk5om+JDvPFSu552HvKiREAiIBGQCEgEJAISAYmAROBSQOD0T9uXAgIXcI02EzdBYtKLtUoeJJGzyLYJbZOPuxudAzwJ89GgcnEiys8DF0dH0ku0lNcYFT8lm7zoT2iQBJEK99EgfIxiA70UP6OM4ipMIvKD3Sb6F5osoXESWiThxyQ3iYBEQCIgEZAISAQkAhIBicCljoAkSRf4EyC0OGHe7gq5qagxUKE3Kj5KguAIEztvtSsaN5dG3yI3ZyfCfTX4atyo1BsRbUSYb9GPkPVxd0Xt4twoL7RUXYO9CfZSU6TVU15jUMKIC58ofw83gj3ViiZLHMtNIiARkAhIBCQCEgGJgERAIiARAEmSOsCnwNnJEU8nR0TEu0BPtaIdEkodQXycHB1pqd+xESJhdhfUIC+0QM5ODqdEsxPR6gRpEuRKmPcZLRYlNLiQd3V2RJCjlv13AEjkFCQCEgGJgERAIiARkAhIBCQCFwwBSZIuGPSnDiyCJzi38BITYb8VjZHeiNliVUiQt7srXipXpd5SXpjraYWGSchbrUpEPCHvrXJVzPWEyZ7cJAISAYmAREAiIBGQCEgEJAISgbYRkCSpbWwu+JUSXS05ZTrFV8lotioaJhEBXGh/hO9RpK8HAR4qJTCDIFOlulqyynRU1AiTvSaNkZuLE34aN6J8PZTygi9MTkAiIBGQCEgEJAISAYmAREAi0IERkCSpg96cAm0NaUVainW1mFrmUTKYqao1UWM0Y7Z6EeiporhKz/HCSspq6jVO9ssSuZiqao1KTiYRTc/fQ2V/WdYlAhIBiYBEQCIgEZAISAQkAhIBOwQkSbIDo6NUy6sNZJRUUVRVq5jMtTYvQZwEMRK+RcIcT9RLdIZm0e5s7US8PKGJEoEbhHwfF+czhhG3tZWlREAiIBGQCEgEJAISAYmAROBSQ6CFB8yltvyOud58bQ3C1E74FJ1uE1HtCrU1HC+sUDROwuTudJuQFyZ5+ZU1pxOT1yQCEgGJgERAItDBEUhj5StP8teHP2Dl4QJqf+fZGkvT2frta/z9o22U/s59y+4kAhKBiwMBSZI62H0SJnRCk2QwW9o1M53BTGFVLTXG9skbzFbyK6uxnIGAtWtwKSQRkAhIBCQCEoGzQMBcW0XSJ7dz+0trKGyr3ZGvmTvnTm56ajEHS6oxtypXxokta/nlf//l6bn/x3fb0ihvVa6Nk6UpbJj/PDfddBNznp7H4uRKO0EjFQWHWP/5//HJ26/x7EfbJVGyQ0dWJQKXCgLS3K6D3WmR90gQpTMohRpnXWuyUGUwKQlm3VqGumuUaqoIbZPeZFHM79SukiM3ISNrEgGJgERAIvBHImDWV7Dr8zk8+cZ3bK5OpdrBgbefGE9wy0F94+ju+BZPfXWAomN7uOOph5nUMwiv1p5YtGno1RpUKm/cWvZzmuPSzH1s/OFdFiwpRNWjGHP8VcxI8G5oUUNVeSZHd1ahCvYhqmcIwpPXUJnHzoX/ZGGKP2Pn/Jfrup9mAHlJIiARuOgRaO0r56Jf1MW8AEtdHac3smu+OkudVTHLq+P0pnb2rerEGO1lYfYNZV0iIBGQCEgELk0E9DnsW7ecxUv3UHyOCFiMNeTu+xmnq97mbZ9feePd51g8eTxzeoKI3Nq4hQ7kxgdvYsOyZ/h58ccUdR1Gv4fG4xWobhSxr4T26UqnkADc7U+etl5K1vEUDuxwoPuw25g5dwaj+gQ0tdCVUXLsAFvLTehdDrP5i5c5/gWYaspJ37GY7UXBHPacytgXR2DXqqk9xfz6xnMsPmbAGDmFxx4YR2cvFTIBhx1El0xVaDzXsOTLdRwP7MqAK67j3lFRl8zqL/aFSpLUwe6gh5uLktuovdNyFUloXZxwavYL03Zr8TskZEVCWrlJBCQCEgGJgESgXQg4qfEJiaFHggV7wzT7tjVFx9n+41v8kBFM5OibePzKzvaXlfrgEePoccUtDNUMJSR4B35etJrQ3LPbNG6b8i1bv9iJo4MbDm1SjGB8vTxQn0XQVv3JQ+zduYVMv4FM+8s/ePAaX0p2bOTTNU4MumUKncvyObJjN8YuV3DXw+OJETO3VFN2fB3r0lSoAgczYoQ/rqesruFEyhJefucT1mfWEn7b5TxqaW2N6ax5fQEb0gta4BnE4OnXM2FYPCGnWVNN1k5Wr/iZNQfLgFB6DJ/A1NmJhLc1p9Octxp0ZG94j9eWZQGOODmP4C9vXY9QlNlz1za7SF/Ff7/aQEZhVRsiXRl702TGDu2MbwuJooO/sHL5cnZWdmPohElMHd0FjxYy9ofGsiz2r53P5xvL8A0fwJQ5NzKoRadWYzU5G97lP0uzcHBypvO1T/HI6ED7bhrqx/np6U/ZVKZr4dMWxujbb2V83wh8XVo0O7mN+T+uYcfRohYX6g8jB07kyklXk9CoHnXFVJHFwY0f82VhV45X+DOs7+30tiktW+1FnuwoCEiS1FHuRMM8PN1clESxItfRmQI3iCburi64OQuS1D7S4+zkiJ9GJUlSB7vvcjoSAYmARKBDI+AaQMzACcre1jxLj22AfW/xQ4EPgYOncd99w9sSBRKY9peE01wPZ8ScJ3ihTwZBgwcQ6t0WJfFF467CteFhVnd8LT+s2MjeDBd6jb+aK8b2J7KZiqmUtB1bWL/XQOi0O7lpYjzOJ9eyfuGLvLEqgAkVtVwed4BPd+uJuPwmHrnvWkIBszaHvfO38bbKE6+R13L/xO54tTH7oys+Yluu+A2PYuwV/QlUu2L/C52x+jUWbtrNyv+tZV9+GdXN+vFlw6H9rB09jVuuu5wRDaZ+NpHig8tZtXo1W3ansCdpB7vTtUBPJpqjGXWWJMlYVUzyjy/y1Q49Rck/8e124SXmgoublUkNJMk27mnLgj388M3nbE9py8tsDG4DB5HYCkly9fDHy6GCo+v/x6+H89FzJ7PaIErGskx2L3mL5/7vJ/ZrB3D381e1aoJZZzZQvOd73nsvCUcXFcPjHuTh0YHNCN/xJf/k861HWfPhSg5U1WBstsAAtqUfYeXwadxz8zj6dfJtJMRVGdtZvuhTvtmS06yF7SCo22627MvmxplXMHlkLB54ENmjP8PHD2DFe0fI2r+RDcem0zuxrU+PrSdZdgQEJEnqCHfBbg6CxAR7qSmtrlVCe9tdarUqksp6qJyp1JuUPEitCjWcFG+E3F2difDV4ODQrvdDp+tOXpMISAQkAhIBicAfhoBnj0nc1gPIWMOb87aQW2ofmbWAfSk5lGFk95IP+M+J5fi5gtCwbNi2l5RcZ8ZZg4nr35wklR3fyprlK8lRDeKGa66ke0AlR5KS2bZ6L6UOw7G6euCqdkPl4obhyPfMe2y7sj6LvoLs3dvQerhz5eSxRLS56mOs+PYQZrMVIsdxRf9A7P1/M9a8zrzX/8uCLUWU1/Tkqjl3k9jZZiqYy475P/Hrph+Zf/AomdUmnr/vWi7r7FM/WnUmu1d+y/uf7qSuV3+69YinOD0Jof85681SgzZ9LR+/9jHf1l3B3TPGM3D7AnafdUf2DUIYdsP1jO7TUgMTxcBB0TSswr4BPjFDuPIWT1Tqt3ltwQrefENor+7khpZESZfHsQ1f8vzbSzmgG8xfXnyEu2cNIqxZb+07SF36NC+8+gbf7dFTa+rHdY9Poa+/poEIZbLhne/YvHIhybuPkIcTr955OX1C6/VbNdU6jEaDMpB/nwlMGjeSPmFC5VfMkdVrWLsmiV9yCzE5OhDddQ5Dg8EjIoauvQYRzR4O5Z5g185jaBMHtUmy27cKKXU+EJAk6XygfJZjiOSwUQYP0ku0VBtaI4Gp8AAAIABJREFUj+sjuvRwc6aTv4eSTLagUk9maZUS9KG14RSC5OZMjL8nfpqzcW9trTd5TiIgEZAISAQkAn8gAuW7WfjRSo5WxnLVtd64+fjhV2dnf1ZVgBErZvwJCAkiOMQPT8Ct3B1XEcTIN4LgsGAC7LRI+pP7WPPNlyxJdaTH9Clc1SsAbfZGdqxZzJaqaOJnXc+dt06gi3Mc7o+FkXqiuMH/yoC2IJXjO8tRR8Qzopc/2uRFvLLoEK6qRG7819XE2aAo3cX2A1aUALV9BpMQ4EJjjKSKvfzw3nss2lxEub4Hk+59kEcen8HQKD/qva0K2Bcbis+zH/DT/hQ2f/cdv/TuQdewIYSKpTs6E9p3IrMfmESXYaHUrv2S9J/PkSThgJt3OMNueZHenYcybWQ6pc/+VpIUQMKkm7hn+kA62eFug6atUhXSk9EzH1AuC6L0xhuiakeUqvI4sv5LXnhrEbvLu3LTPx7mrnMkSJTtYP5rb/L9bj215gRm/P3vPPb3a+jj695Akk4yIsiHec98wqrMZFZ+vpCx/eIJv7w7/i2Umd5dhzH5lr8yM0HQvzJSY/0wlxQzf18Wycm72Hx4OkODg8A1iOCoWOJjYVdeHid2JXGsahCDxAdWbh0aAUmSOuDtEeZznfw8FEfW3IoaRaMkksfaNuFP5K12JdzHnQhfD0U7VG9y50BuRTVavamZqZ6QFxqnMB93pV/ndprm2caTpURAIiARkAhIBNqNQHUJ+es+5RnjZrwCEpn+wFiiz9j4BCte+obdBlN9yO+KfSz+Yh3HK6PJ7fklz908gXCN3SPL0c85tmkHu7UJTJh9L/dc1Qt/oXT6sZyUfVvZ792f7j26EduovtCTd3Ata5avY29BJ9z2LuezVzeizUkmKSmVyog+9Kw6yNL3X2hlprVYXDV0HjuZiLyjbPzsGQrKNvJdsR/B29eTFRXE67cl4kcdZUlb2Ge1KgGYEoYOIMClydQua8N7/LRLECQIHjaTux+cakeQxLAh9Jt2C9N3b2b/yQxKspPYvms/V16WQGi0CtQRJEy4gYQJQjaN1WtbmWp7Tzmp8Ywexe2PjQJqqSpKb2/LP0TORpTEC93/LFjOG/OEQ9Sd3NBfTfb6r3hu3gI25IRw/SMPcc9N5+Z7JSaetuIVvttbS60ZwsbezkN/m0xfX3eaXI8iGHLLX5ixeRXJ3xaiTdvIul3HGD8wFv/gFiypGRJ+xPXqTlyvENh3kqLySnKLS4AgwJPAsGjievtDWgFl2ftIyYNB3Zp1IA86IAJ23zgdcHaX8JTUrkJL5KmQodJqAzUGMyLynaODAxo3Z0UbJIiPIEdiEwEfov098VK5UF5jpNpowmqtQ5jvCXl/jUohSjJgwyX8oZJLlwhIBCQC5wmBOrOOsuNr+ea9VRyp2EoXtSedx93G9f0aWcspMxGRV5UdqMhIJddkxBzii7ezEy0fVvJS9pBVXox7v27EhPgrWiTRYbWuHEOtDnw0qNUqJXS3bSBVaB/GXvcAYRXiTCVZe1by6y+HMfabwKxbriHRMZ20EhEp1oQ2P4XtS38i2RBM97HXM33oAK59ehThKYtZkZLChhVpxD3zT+YM388h2wB1kLZjNTlWC1bCGTMoFjdXW0y7Svav20hGlQ4TED9hBPGBNg2SrQNRhtBveG8iVm9jd2kZRw4c4mhGIcOiOzVbi32LP0tdEKXLGjRK/1mwkW8+1aDOceHnT75iQ04w1z30KPfffgXdz1kDU86un9eQbTIrJLb31PF01ajtCJINyQiGje+L/6/HSK0qZu/O/aRPHUrP4MZoDDbBZqVeX43YxeapVuGj0TRe9/T2JzhUhADZTZUun5w8HXQ7XYiKxqaycgERaPm9cwGnIoduiYAgQMFe7gR4qBBJYC3WOiUynTAlaE0bpHJxIsxHQ5CXGoPJooT5FgEdbFqmlv3LY4mAREAiIBGQCPzuCGgCCBt3F89ebyU64AfWf/0vvsz0Im57Ebkj+zHmtpmcypW6MOmfTzOpYTKZ/zvET9szqQwdxfiBEfjYa5HII2lDMkV5zvSb1pcuYbZIc9VUVxmo1YNPuLvyoNq0NjXhCVcwO+EK5ZQ2ez2L5u3hcHRvEmbfz4N3jqObePg2VZJ3dDNff1RMZlAC/t280QV1ZuysuQyPMFHTF0pe/Q8bDUbyk5LxfvkZ/mWnJistyqE+JUcEAX6ONBluZJFxxIRBL4YPIiYyALWq9UewkLDOeHkLvVgZZbmFFJZWUMvFQJKK2fvzF7yfuRYfm2omeiiTRw2gZ1j7CEETUfqMBb8sZ94bOk5WhDBz7t+47/YrfwNBErinkZpch9Ui6qF0jfHHpY38khGduuGuER+IKoqzcinS6jCemtFL+Swpf/S5JO/aS/LBfEVz1Cm0E/ExTQHi3Tx9CAoJx4/dVOqqOZGbB3Rtai9rHRKB1v9DO+RU/7yTEu+t9EazYlYnEslarFZFYySCLAizOqFVEnXbJnIcCV+lylojNQZTIxlqlHdxQuNm+4ZCua4zmBr7F2/qBHkSGiZvtZsSQtzWtywlAhIBiYBEoOMgYLVaqaqqory8vHFXqVQMHTq0YwfgcdbgFz+cB17qz9BvrFjnfcrKH/7DE2t6MSMykQXTO507yAU7+TW5hDzHfkztG0uYiNigbEUUZGupKIGI4QEE+bWutTIUHWfLDwtYvKeEiFl/5+7ZgiBVU3QimS2Ll7MxZT+bD/oycfo9xDgu4qG33+INrzDqrnZk/fKfWP1+FjH330tnlRVds7BoFZSXQn3aQn98vB1pipFUTmmeBYtQIxGKn48rLk0/0w3zv9iLQnYseocdi+zWETeWgxNncfc9kxjQM6xR42cncUpVpfHGP9CD8uJU9u7VExbfFR+/EPx/c0C4Uopz6rAq3guCxDrhZFP0nTKLM5+oPL6dZfOdyV6ngppMdq5aw/ZjFqL6TGDK1EkkRjdpklCp0Hh6IQKRH6uqJi87Dx1dTxvu/MwzkBJ/NAJNT95/9Eiy/1YREISnuEpPgVZPcVUtVbUibGgdTg4OeKhcCNC4EeqjUbRJwlRO+CaV6mrJr6yhRFdLlcGkaJicHR3wVLkqciHeagI91IrWyWix1vdfWaNEzKuqFaQKhLwgYAEeakK93ZXgD61OUJ6UCEgEJAISgT8UAfHiqra2ViFBFRUViF2QIltZUlJCUVGRsot6XFwciYmJODt3/J9wV88gBs56iacsFVQ88z07tBn8svk4nDNJ0nFi9WqS8grwHnkbg+Ij8W+MRVSFtrQWfZU3YYFB+Pu0knxWm82+VV/wzrIDVPa4m0dumUliGFQXHWXTwrd49/Migq+7nJkjE+iv3smKRXq6RalZ+8FTaA8EsuOEH7c9/BQP/HtyU7AG5dMhXneWUFIgTAbFiSD8m2mS/tCP0IXtPHQgM2+6i0H2eZKq09m2ZgeHUtezMDWHPJUrz8+9lhFn0ihVneTgugXM+/AHdlV2YeL0EGpS9rPgtXfw9n6AW67pezp9znnFofTAKuaLvXFUT6L6XM7NjzzE7TNGEGvHkXBxxdVdU0+Kag0Yy7WI4O3t0681DiAr5xmBjv8Ne54BOZ/DCYJUpNVztLCCsmqDQnZs4wv/o0q9sWE30SXIC38PlSKXWlSpEJ76L+L6FoJYldcYFPmKGgPWYBS/pcIqPamFlco1W9+iFPLC10n4L1XoRThLHwI9W/lBsW8k6xIBiYBEQCJwzgiYTCa0Wq1CfmxkqLKyUjkuLS2lsLCwkQwJOYPBgNlsxt3dHR8fH2Xv3r073bp169hapBYIiVw4A2a/wtMVQfycp0I1vO0A2i2annJYnbaeL37YQLpLV26ePpaB0QE0ciR9DdVGI7X44+XhhaZlhDVtNvvXzGfex9+w4oQ7w6MqSNvwFe9vAG32XvbtzCDi1ud55I5ATnz6FZ+t+4HckJu5f/ZJ/vHoF6xZW0zYsIe4/m8tCdIp02zlhCfeAY44pokf4FYu252qqRFhphtUVBp3VCrXpjXayZ22WlNC1pGdLN+V3UxM7RNE7zHTGRDS7PRvO4iZwMNPKtEkmvqpPMBP817hmY9/YX9+Kr+u38bey4eTGObRmHOoSbih1kCQXn5rAetzgpnx14e546rOlK2ax/PvrefdJ+twcJzLLVf3UcIhnNL+jCe88QkGhwybtq/tBtXVVcr/niLhocHdxeUU3yWv2EEM6d+bWI8yDu/czd4j1Wh8AwkI9MW9pYZKpULt6XVKMt22ZyCvdAQEJEm6gHdBV2tCEJ6WBKnllEqqa3EoAW2tkVKdQdEkKS+qWgpSb1onyM+J4kpCDGpyyqtPIUj2zeo1WbU4UKloooRfk9wkAhIBiYBE4NwQsFgsVFdXI8iPbReER9TLysoUEiTIkNgFMaqpqVHIkNAKeXl54e3tTXBwMD169FDKkJAQpRTnRD0wMPCi0CA1R88JlVcXJj3+XqPPUfPr7TyqTmfD55+w+KgH3WY+wqyx8UTYO/EXFpCtraQEd9QqFW42Kzxb94ZiDu/fw67kAoKDeuNlKmDv3gLbVYIHjCEqJIWf533F+kUnCLr1Vm4YnIjrmrfrQ3SbqtGmrGHh8hvoc33XdpmONXZOJ2J6ueJ2QLi5lJFbWInBYAHVqb+5hflZVGnLlKaeoUGK2eBZv8KsyuHAz29z37OrmqYABMYN5IHI35kkNRuh4cC7D1NmTWT51n0cy9dSW6GlqkaPcMlqeVuUFlUnObRuAfYEac4dk+jpBQbvv/Ev6nj23fW8/Q9wcHiIm6/qfQ5EKZau/RxxEnlgTcVk51ZiMvsJ05pTVpCbnYq+pko57xMVRqCX5hSiGtDvKu56ci4zuxSz8Yt3eO6NhazfuIiP3L3xCniY2YkhTcE2TEaMgsSfMpI80ZERkCTpAt4dEa67Qm9spkFqazrCFK9MZ1BMndsiSLa2gvgIU7zyagMGi7VZlmmbjH0p5EXyWmHCFxNg/4tjLyXrEgGJgERAIiAQsJnHCfJjv9u0QsXFxRQUFCi7qAsZvV6vtNNoNI1kKD4+vpEICQJk2wUhEnJys0egiIM/fsz7v1QSNfpunrh9PD3Dm/9elWccJbM4H23QICIiQwhqqUkKjGfYpFk8UhOJNu5Kps2aSA/h52KqJP/EIdYsXsSab+dx3H0GEx68jmtvCyH1ow/5v++O4ujnhlofQefIHL5+8//o1eUp5pyijvHEy8ehwQ+pkiqdlTo/EcparMOfnn1jUS/Jgaoctm3az8krexDl7U0zmqQ/ycEDx8lXTNeC6RffhdgIb3sg2ldX+RGZMJ5bb22uMvIMjqbP6YO0ta//9ki5uqF2clY0MLXualSuLqclSC+9uYD1J+s1SDaCJIZxC+rGiOse4ymzgcdf/5G3HgUHx4e5eWIvxcenPVOplwkkITEOp18OgimT9WuSKRkfjo9KRTOaVJPJrt2plFUIShfO0L5xhAWexjDOI47Lpk9lysGD7E/bQMqODaxaOYxRfabQxZbaq7YWvbYShfq6OOOsUdPy49n+dUjJ84WAJEnnC+kW45gtVoqqajGKrNzt2PQmC7UmM0LTo3Y5820TWirhr+SrccOlKbxOmyOJyHk55TpJktpESF6QCEgELjUEhHmcTqdTSI4IniDIjq0UvkH5+fmNZEj4EAlZYSbl5uaGp6enQoYE4enVqxehoaHKLoiQqIvS19cXx3Z8P19quFO4j7XJRtzcwUl7hPS0FJwObuerZ9dTM/JWnn58JsOi/bCc3Mf6A+kUVAiTccjZtJzDGfkEDu5Fj24xrTxAa4geMpMHhsxsDqmlhBNbvuKDeUtwnnYfzz7+CIkuu1jy3lt89sFWDFfMZMzBT1lRPISZd7jx7Yvz+ecLQQT/6x6u7WcjIYIJBRMcbiNFB0nLsGAOA5udVrcxs+j/2QHySvPJ2baM71bHEXzdUGJ93euJkj6X/avm882yI5woAM9OY5gwaii9os7hcdq7EwlTH+Xzqc2X+puOCvexZEuqkpLEr9dYRsYHoXGtJWvnr2Sqe9EnLhxfdQPlE2vZuJPk7CKEPiYothMRQQENSXPtZlGVy+F1C3jxTZEHKZgZDzzCnDsmKhokOyncgroyZPojPF5ykpc//5F5D9WB46PcfEWPVu6zfcvm9V4T76b/+4+xNVtPxrpv+Gp1JPdO7keou0s9UarJIunHD1mwMof8cvCOu5opo/sSE9Ro1Nm8Q9tRSDyDEhJIDNvJirw0Du3bzY4To+nSq4HgmowYavWKH5KrpweBUeG0HlbE1qEsOwICZ37a7giz/BPOocZkxmCuD9PdnuUJgiQi1Cmhv9sREcdktVJtNCvBH9pDkoQ2SfhACS2V8tKrPZOSMhIBiYBE4CJHQESPE+ZxguAIAmQrRV2YxwmzuLy8PGUXxEgQJSEvyI2Hh4eyCzM54SsUFhamECD70t/fXyFNFzlM52n6hexfup30PZ/xxIZArp82hdSNy1nyCRTuKSIw7mrm/v1aEqP9FI1EecEhtq7fwK6dO9hzOJ28cgMekb24bvQw+tuFX242eVM5J4+ncPBILrrGC+Wkp+gI7H4lo2+dRXzpEj5evISvf8ql56zpjBw7goyD74PKhN/gR3jsyh08tOA1brdYqLutHwF9L+eyBi99Dy9/HCgCTnAsXYt5iDe4NPyqxl/FX29eTUHOUnZmL+ftl8Ch8hoSY+rXQ8UevnnvW9YkZyrEIiS+ExFhATTpkarIO3SEIynZlFPIgQMZiGB6IudTfnoSq79z46ibH6GxPRnY087Uq3Gd9hUzBl0OySt2k42JWm0SWcplK1ZrGpu/+06ZA/59ufyyLng7OVK89mXm/HWpQkpj5v7Emn9OIDawlO0f/YP3ayZw9egBRPs1PKBU7Obrd79n79EiPDr1ZeqYofTpJMKaN990qeuY/8VXrM8JYWYbBMnWwisygYl/eRMH5vLiZz/yzhMaYga8zbSmSNs20bbL3jN48q5lPPbSeg6nL+b5Jx1wqLiKeD/3+lxcpVv56NWFbM0sVkwDI/rEERXgzZn1uqH0HjGEwSPWsHHRIY7sS+LXjUe4ptdQhLLSrK2kuCAXESA82FNDXERo23OUVzoMApIkXaBbIRK92gdeONM0hKZH7O3fGr6Uz6KJIiom1RSztP3DSUmJgERAItCBERDR4wQBarkL0iNM42xESNRFUAVhOif8i9RqdSMZEtqfhIQEhQyFh4crpSBE4rwwj3OQ352/4RNQTca2T3julpf4xaETg/7+Fk/eP4qKX/7Gyx8uJcktkUlXRWI6mkV+gAedAjzwHXgzTw+8mfI9C/n/9s4DPqpi++O/bEvZ9EroHVGQIlVAioAoooJYsSG292zYC7ZnL1hQHz7l/UWfigJiVxBQVASVbqH3HgjpPZvy//xuOPHuskk2IUA2OfP5XGbuzJkzZ753s8zZmTvzxoxvsXxbJhr1H4erLxyMTtxr2SPkHNiETau+wofTZ2PW93vgPKkz2sfJDEEkGvc8FWEbP8ZrC2bis0PdMOaeybj/xq4oXPs5nsqwwmKLQFzjDhh2x/1YsfUFfPTLHEzeOx0FZ8/E4if7whkAtD1tMGIss7Efxfhry3YUGVNJMtSKxYBbJ+GW7eFoufInfLX6O7x63zduVobGxcHhdCIwJwdJvy/Ft18kwn7gZLRpfzI6tMvG8pmv4IknP8JKt1p7sGbha8aFuB4456bH8X+Pnw2Z43ITLb/JR8b+7zHl4uvwYXkeE9ym/Fs8ffHhd5n6vIA1392GTiEOJO3bjpLisl0n0rJyUGTspR2MJt2GIP6dd/DkZy8hM9+kLLQFOvcbij5jb8ENF/RHxyN9JGQVO5DYtjduGj4Wl4wbUbb80aTCMxnW9FSM+OdrcDgfxid7glGUDKA6ThISMHzSc9i76Xl8u3U5vlzxKR6/abZbM+GJicY7bUX5+dizbCG++jwMGRvboP2ppyK8kk036Ax279IFHRf8hZU7N2Hz8uVYcag7hsQGIjMzFXzXjG9khTlj0bxJlFubelM3Cchfbt20rh5bxbOPeCgs/0/1xVniGUh0YiwW3+Z5rJYABPGUch/laUeI3ab/ydfjz5x2TQnUdwLcCY6zPHLRIZI0t9Deu3dv+cVNE+gMsZzL4+jkyPtCnTt3Bp0g80VniDvMWY/mYJX6/gCOqn9ZWD93DQIGDsbQ1lfg2X/0R0J4IBpf9jpeatQKYe/+gp+n3oGp2ztg7GPXYnjnJsYv9GVNRqDLkIvRZUjZXcpfi/D1X2JMJJp2aIe2beORted3LF74PdblxmDAtRfi/Muvx9iuTmQn78bWdUvw04Lv8dHDy2A7bwhuvHgs+oSk4devv0Lylh+xZk8IggZ0RsdEJyLjL8ST0yIQPt2FET1W4KWVqcYyKicC0GjAMJxmmYO5KMbmjduQVdQbMbCZVmicjIteegMXrZ+Ne179HOt3p4uhRtyka1eEH9iM3+Z+j9V7F+PDZxfjQ7TCBQ88jUn39EdC+27oPzKrYgcovC16tI87YpMBt0aMGxsczmboNnKkYfuR5YdzOrRGuNVi2H/q8H9gxKqvkZJVgDaDOiIyhFswhGDALVMQ18iO+M83YGeaSVPTYbjhH2MxrEuTI5fZHRZL7Hkpbu95qalS1cmwxp1w/gOf4vwKRAOsdkS07YeRI+PA9MktPZcrdsH4dz/A+D+m49rnvsbBDLNnB7Ts0wfBG3/DD3N/wl+752HqA/MwFSfhmin/xo1d2uG0fkNREJeBRt3bITHctLQnrAN6DDsXVxxKR6NNQFRCEdL35QOxpUg9uAc7Nu0DHE0RmdAZbXUiqYKnV7eyA0r5BqqGE0Jgze4U7EzJAs8yqipwORwnkujzWHz4tZKPVeR9+XWTy/hOahSBjon660ZVz6K+lN96661YvXo1Hn30UQwbNqy+dEv7Uc8JcHkcN0HgrnB0cCRmmrM/e/bsMS46RJwV4pI5vi/E70E6QdxOm3F8fLzhBDVr1syImzZtCl7Mt9e/Uz6Py6ciZeMiTL93CO5Z0g5db3wDvzx15t+7e9WCBetm3o5XPvUYiFeptwPOvPoKjLu8F/i60JEhDX999TZefXgqVkT0Q/8zxuKWSX0Q8PMsTH5qJtbs24RlG9MR3Lgnznn2DXx8ZecjVbjlrMC/2vXHM9sKUNBuIr5a8DiGNQ2Dw7ffNw9r2or5L7yFmQsXY/m6zdi+JwxDDSfpUvTQ/6LdaB+7mw2Yc+8UfPL7aqxavQE7kxNxyZR/4/7xQ9DBfb+Qqk3I34zv/u8F3H3LNKxt0hujbn0e0+47A9zTQ0PdJqAzSSfw+TSJdCI1J984q4hOUGUhMthhHP7KzRgycsveHapInoOB6JBARIU4sD8jD3z/qbJAnys00IZmUZXs3lKZAi1TAkpACdQiAf7Iww0Q6ACJQ8Q0L74rxA0Tdu/eXe4M8V0hOkOU5fI4OkJytlDPnj0N54fOEJ0giYOCgnTmvBafGVXZQ6LQvPNgDC5KQMsIGDMUsrlXbTR18iVT8NYltaHJrKMY1qCm6D5iEsbfcy36ysj1zFvwZq/RWPftf3DL1L/QqPVo3F+lg0S9PXDOde3w5qPrkLRxHhb8eRf6J4TCUS0vqQ2G3/Mcht+zHQtffgszvtyFJq0TYJ60MPdA08eCwEm48Pk3cCE24rMHp2DWrzk4qamX8498aLpgz3Zs/GM51sCOJk1aoGefTuog+cCtLojoTNIJfAocCGxNzsTW5CxjUwZvjhJ/fOJSu7Zx4UiMDMG+jFyjTm5hkddlenSQ6PC0jg0DnTCek7TlYAa4O563QPmwQDs6NIpAy5jq/jziTaPm+QsBnUnylydVf+3k8jg6NnxfiLFcXCbHrbPpCMnFWSE6Q3SSuOSNTpC8L8RlcXR+eDVv3rzcEeKmCRqUwHEnsHcGLut5HT5JykPr2+di3mND0DzCYVpyd9wt0gZPGIECbFnwJibfdzveXN8CfS6ciBenTMTp+tV0wp5IdRrWmaTq0KplWToorePCjV8zuf02t+12FZcYy+Q4u2O3WhBstxrOS7PoUATarGhls8EaEICdqdmgo0R5TkJxGR7lnYF2NItyGlt5Wy0Woy517UzJNnbTc5e3wmk4VOFoEaOzSLX8eFWdElACPOC6pMQ4LJWOkDhDEnP2h8vjdu3aZThDXCLHd4eYTweKsz10hLg8joeocittOkHmi5sm8CBWDUqgzhBoMhAXje2N/SvzkJ++D2nFRWgCR9nuaXXGSDXk+BBIQ05eMSyO3ug9sjsGjz1HHaTjA75WWtGZpFrBePRKcgpdxtK4lGyenVQMm9WCaGcgGkeEIDTQ7rYshE5RdkGhIc/lenR8HDYrYpyBaBQRAqfDfQMGzlBx+/B96TlIzSkwdsmjfFxoEBpHOBHkcDvK7ug7oxr8goDOJPnFY/ILIzkrzjOFCgoKDEfIHHP3OO4ct3PnTuOiU8TlcnSGOHPE93+4cQKdIZ4bxNmgFi1auF3cNIHbbWtQAkpACSgBJXC8COjPb8eLdBXtOB12tI3jxR31Kw+cGQoLchgXTCcoVFSLGz2EBzkQ3og70WhQAkpACdSMALfEpgPE94UYy8WZIS6HoyO0Y8cOI+asEJ0hbqbAQEeIF88U4vK4Xr16oWXLlsZFp4izQ3q4as2ei9ZSAkpACSiB2iegTlLtM1WNSkAJKAG/JSCbJtAR8ry4bbY4QnSG+L4QnSEeuEoHirNCDofDmBXiMri+ffuiVatW5c4Q09w9jjIalIASUAJKQAnUZQLqJNXlp6O2KQElUG8IcDkaNyNg4Ps1J3KbaTpCdGroBNEuuXjPrbTpCG3fvt246AxxiRydIZbx/R/azis2NhZt27Y1tpCnAyQXd5Hjxgp871KDElACSkAJKAF/JKBOUh17ahy8cDNwDi18GWBUR77GS9wIAAAgAElEQVTsrKVSsAHq5jI8HcPUsQ+AmlMvCXA5Gs+kuu+++4x3bp555hljedmx7iw3TaADxE0QzLEsj9u2bRt40SGiY8SZIb4rZLFYDCeIDhE3TaDz06VLF7Ru3br8Yl5ERIQhe6z7ofqVgBJQAkpACRxvAuokHW/iXtqjU1RSUorC4mLkGTvWlcJmCUCwwwaHzQJrgMXNmeHGDcWlJSgsKjHki0pKjI0egu3e5ekc8cBa7p5XtnEDN3qwIMYZZOyGx7Z8cci8mK5ZSkAJVEGAsy9fffUVJk2aZDgqN9xwg/HuTRXVfC6WWSE6QnLJLBF3iaMTtHXr1nJniDvJcSMFvk9EJ4gXl7/xPaHTTjsNbdq0MRwhiZlPp0mDElACSkAJKIGGRECdpBP8tOkg5Ra4sCctBztSspBVwPOPyg6W5flFTaOc4Pbf4UFlO9yxjAfKUn5Xajay8l1GDzjzFBZkR/PoUONQWG4Fzlki7nx3KDsPmw5kIDk73+1sJTpHjSOdaJ8QgcjgQDdH7ARj0eaVgN8TMP5Ws7Lw8ssv4/XXXzeckClTpqBPnz7GBgbV6SB1cVaIzo/nxd3j6Phs2bLFzRnirFB6erpxphDPFeLyuOjoaMMJGjhwoLFMjo4QL26gwO22NSgBJaAElIASUAJlBNRJOsGfhOz8QqzalYJD2fnwPEyWztD6pHRj9qdjYiRiQoOM9Pr9aUjKzHOznG5VZr4La/enGc7QqU1ijDOQ9mXk4I89qcj3cphsUUkpdqdmIy2nAL1bxyMqJNBNp94oASVQMwJ0aLhs7bbbbsO3336LAQMG4JVXXjFmaCqblaEzRCeI9c0Xt8rmTnF0hOSSpXJ8z4l1qJfOUFhYmLF99tChQ9GuXTvDGeJ7Q1wep4er1ux5ai0loASUgBJoeATUSTqBzzzfVYTVFThIZrMOZOUZszw8A+lAZt4RDpJZlpNQB7Py8OfeFCRGhGBjUrpXB0nq0LniGUq/bTuIIR2bwGHVZTXCRmMlUBMCdFjWrVuHq666Cps2bcI///lPPPjgg+VL7OgIyUVHyDxLRMeKS+PoCG3evLl8dogbJ+Tm5hqOEJfG8kwhnh3E94TEEZKYu8qdyE0hasJM6ygBJaAElIASqGsE1Ek6gU+ES+Y4++M5g+TNJDpHh3Lcl8t5k2MeHSUurUvLLURBUXFFYuX5dJTyi4qxKyUbbeOrPqepvKImlIAScCNAp4fvH11//fXGznFTp07FRRddZCyv4/tCDFwex40S6EDJJUvlWEYddIQ4K8TtsjkL1K9fP7Rv395wiBjzwFVuqKBBCSgBJaAElIASODYE1Ek6Nlyr1MqB0N70XOQfHjhVVYHL5fJyixBotyHYbq1K3HhXKbugENHOYGMTiKoqcPOHnSmZ6iRVBUrLlUAFBDiD9NRTT+Hxxx83dq57/vnnDeforbfeMpyhjRs3GjEPXRWHiaq4QxyXwnF5HB0gXh06dDDeFYqMjNRNEyrgrdlKQAkoASWgBI4lgYBSrvXQcNwJcInb0q0HkJFX6FPb6bmFyC50ISLIYWzQUFWlzPxCpGQXoFF4sLFLXlXyLA+0WTHq1OZ+s9Md39NYvnw5vvnmm/LNLnzpp8qUEViwYIHx3gxnKfjivgbfCXCmhw7M+PHjkZCQYJwf1KtXL9ARCgwMNHaxkyV11Mod5Fq0aGHMCnFZHC+ZGWJ9Lo+THSYZy+W7RSqpBJSAElACSkAJ1CYBnUmqTZrV0FVYVFytgT23B3exjnGKkm8NUbbEN1G/lMrIyDCWNvGFePX1q/8I5X2YOXPmlA/Qq6+lYdagE8OlcH379jUOhuXs0IYNG8o3W+jcubNxwCq31KYzRAeJzhM3V2Bdz7hhUtReKwEloASUgBKouwR0JukEPRsun1u8ZT84Q+RL4MxTTkGRsWNdaKC9yio5hUXIzCtElDMQQbaql+dxu3DOUg07uWmVuuuKAB0jziZlZ2fXFZPUjgZEgLND4eHhxiwRl9rt2LEDs2bNwvvvv29susCzh4YNG4Yrr7wSZ511lr5D1IA+G9pVJaAElIAS8H8C6iSdoGfINY6rdiZjd1qOcZZRVWYUl5TCVVwMq8UCuw870HEzCNaxWS3gGUpVBcqdkhiJ9gmRVYnWuXKdRapzj6RBGMQZIXPgzBwPaOW1bNkyzJgxA3PnzkV+fj6aNm2K0aNH47LLLjNmlnT3OTM5TSsBJaAElIASqHsE1Ek6gc8kKTMXa3anlB8IW5kpQXYreLhsnqvY2LK7MlmW8fDZaGcgkjLyjJ3rKpPnUC8k0I7BHRIRbNcVmJWx0jIl4AsBl8tlbNmdkpKCr7/+2phhWrt2rfHuUe/evTF27FiMHDnSONzV09nyRb/KKAEloASUgBJQAseWgDpJx5Zvpdo527P5QAY2H8wwnJ+KhDlz1C4+HM2iQrE7LRtbk7Mq3dqbu9+1iQtHy5gw7MvIxV/70sB3oLyFMgfJhp4t4hAbFuzTrJM3PZqnBJTAkQQ4y8nzjXJyckAn6dNPPzU2GuH7dNddd51x2GxiYuKRFTVHCSgBJaAElIASOKEE1Ek6ofhhLLXbcSgLW5IzkVPgOmJbBrPDw9kknnu0I4WOUiZyC8vOXTF3wemwGQ5Sq9gw2G1WwzniGUtbDmYgNafATb/VEoCEsGC0T4hATGgQLB7Lh8x6Na0ElMDRESgsLDTen+PsEpfj8fwjbvAQEhJydIq1thJQAkpACSgBJVDrBNRJqnWk1VfoKi4xHJ6DmblIySlAYXEJbBYLYpyBiAsLNrb8Nr9bRPnsfBeSs/OMbb5dJSVwWC2IDg1CfFgQnA6723tLRSWlyHcVISu/EKk5hca7SoE2i7EcLzTIbmz9rQ5S9Z+b1lACNSEgG45w4wfz1t810aV1lIASUAJKQAkogWNDQJ2kY8O12lp5WhUPdOXFNJfB0TGyWcrOTPFUyIEWnZ+i4hJjdoiTQHSseFU0IVRCeeoHjFkjziSpc+RJVu+VgBJQAkpACSgBJaAEGjoBdZIa+idA+68ElIASUAJKQAkoASWgBJSAGwGL253eKAEloASUgBJQAkpACSgBJaAEGjgBdZIa+AdAu68ElIASUAJKQAkoASWgBJSAOwF1ktx56J0SUAJKQAkoASWgBJSAElACDZyAOkkN/AOg3VcCSkAJKAEloASUgBJQAkrAnYA6Se489E4JKAEloASUgBJQAkpACSiBBk5AnaQG/gHQ7isBJaAElIASUAJKQAkoASXgTkCdJHceeqcElIASUAJKQAkoASWgBJRAAyegTlID/wBo95WAElACSkAJKAEloASUgBJwJ6BOkjsPvVMCSkAJKAEloASUgBJQAkqggRNQJ6mBfwC0+0pACSgBJaAElIASUAJKQAm4E1AnyZ2H3ikBJaAElIASUAJKQAkoASXQwAmok9TAPwDafSWgBJSAElACSkAJKAEloATcCaiT5M5D75SAElACSkAJKAEloASUgBJo4ATUSWrgHwDtvhJQAkpACSgBJaAElIASUALuBNRJcuehd0pACSgBJaAElIASUAJKQAk0cALqJDXwD4B2XwkoASWgBJSAElACSkAJKAF3AuokufPQOyWgBJSAElACSkAJKAEloAQaOAF1khr4B0C7rwSUgBJQAkpACSgBJaAElIA7AXWS3HnonRJQAkpACSgBJaAElIASUAINnICtgfdfu68EGgSBkpIS5OXlITc3F0VFRUafLRYLAgMDERISArvdjoCAgAbBQjupBJSAElACSkAJKIGqCKiTVBUhLVcCfk6AjlF6ejoyMjKQmZmJgoICo0dWqxVOpxMRERGIjIxEWFgYbDb9SvDzx63mKwEloASUgBJQArVAIKC0tLS0FvSoCiWgBOoggZycHOzduxfJyclwuVxeLeQMUnh4OBo3boyYmBh1lLxS0kwloASUgBJQAkqgIRHQn439+Gm7ikuQW1iEfFcxSkpLYQ0IQJDdihCHDTarvm7mx4+2VkzPz8/Hnj17DAdJlth5U8zfSTjLxJhL8KKjo8FZJg1KQAkoASWgBJSAEmioBNRJ8sMnT4coO9+F5Ox8JGfnISOvEEXFpbBbLQgPtiM+LBgxziCEBzmgr5n44QOuBZPp8CQlJeHQoUPl7yBVpZZL8Q4cOICgoCBj6V1V8lquBJSAElACSkAJKIH6SkCdJD97snSQ0nMLsCU5E3vTcwznyNwFOkxJGXlIjAhB+4QIRAYHqqNkBtRA0oWFhUhNTfXZQRIsdJSysrKMd5U4q6RBCSgBJaAElIASUAINkYCOgvzsqWcXuLA1ORN70o50kKQrXIa3LyMXmw9kILfQ+3soIqtx/STAjRq4QUN1Xzmkc5WdnQ3GGpSAElACSkAJKAEl0FAJqJPkR0+es0icJUrKzENxSeX7bRQVlxjL8TjbpKHhEeA7RpW9h1QZETpX6iRVRkjLlIASUAJKQAkogfpOQJ0kP3rCeYVFxlI7btTgS8hzFRmOkquoxBdxlalHBDgbxLORahI4+1TdGaiatKN1lIASUAJKQAkoASVQVwmok1RXn4wXu9JyC5GR7/syKG7uTocqq0CX3HnBWa+zuKV3TXeo47tI/v4+UnFxsbFpxa5du4yzoer1w9bOKQEloASUgBJQArVOQDduqHWkx05hVr4LOQVF1WqAjlJJFUvzqqVQhf2CQFRUVLV2tpNO8cwk7m7ncDgky69iLjFMS0vD9u3bsWjRIuzfvx9jxozBGWec4Vf9UGOVgBJQAkpACSiBE0tAnaQTy79arUeFOBAe7EBKdr7P9SwBgN2mE4Y+A6sngpGRkQgODq725g2BgYHG9t+M/SnwoFzu5rdt2zbMmzcPX331lXHfu3dv0PHToASUgBJQAkpACSiB6hBQJ6k6tE6wbESwA+FBdqTm5IMzRFUFS0CAcbCsM1Afc1Ws6ls5l9rFxcWBB8ry8uUdIy6x4wxUWFiY3+DgBhOcOdq8eTO++OILfPPNN8bufF26dMGdd96JYcOGIT4+3m/6o4YqASWgBJSAElACdYOAjp7rxnPwyYpghw0xzkAczMrzadkdnaNG4SGw6Xk3PvGtb0IJCQnIy8szDojlTEtljhIdJM4+sU5ISEidR8Ed+LjN+fr16zF79mwsWLDAmDXr1asXxo0bh0GDBhn9qfMdUQOVgBJQAkpACSiBOklAnaQ6+VgqNiohPASZeS5sT8kCz0OqKDhsFsSHBSMxsu4PeCvqg+YfHQHOJrVs2dJYbnbo0CHDieCOd2Znic4R5bjRQ7Nmzeq8Y8GZo+TkZPz111/48MMPsXDhQmOr84EDB2L8+PHo16+fX82EHd0T1tpKQAkoASWgBJTAsSIQUGoeMR2rVlRvrRLg5g1bDmZgZ1o2eB6S+cwkqyUADqsVjSNDcHJiJILs6gfXKnw/VMY/8aysLGNGibMvnFVi4Ls6oaGhxrK86Ohov9isYeXKlbjtttuwfPlyw9nr3Lkzzj//fPTs2RONGjVCRESE0Q9x/sxxZWl9b8kPP9hqshJQAkpACSiBY0hAR9DHEO6xUs1ldCc3jkJ8eDB2pWYjJSffcJRslgBEhQSiRUwY4sKCdJndsXoAfqaXDgBninjRQZKldzabDXa73a+2+6aTxHeQGLjN97p167B161bD4eMsGfvK5YJ0lrh8UGK+a8WLzqDEkqYMd/SjE+XrxXbUsfKzPwQ1VwkoASWgBJRANQjoTFI1YNVVUW7xXVJaCm7UYOF2dhqUQD0mMHfuXDz33HNYvXo1unbtiqFDhxrOT05OjvGeEne587xyc3ONw3XpSMmSQ4nFsaJTJZfZqaIz5XmxnNuk06lifXGuJC2xZ3k9fizaNSWgBJSAElAC9YqAOkn16nFqZ5RAwyDA95JefPFFzJgxw5g5uuuuu4xldzExMV4P0eUGFpyBovOUkpLiFpvzJE3Z7OzscoeKSxZ5iWNFyk6n05iVEofKPENFO3jRuZKYaTpWdKDEiZK0t3vJaxhPVHupBJSAElACSqBuEVAnqW49D7VGCSgBHwnQaeGW38888wz+/PNP49DY22+/HZ06dTIcJToZNQ3UzU0izA4V01Xd07ESh8ocix18B8zsTMkMladTJfeU5ZlVZmfKnKZe872kpT2NlUDdI1CK0qJC5BYUwR7khMNqsrCY+aVwBDtgO4q/X5QWw1XogqskAHaHA3Zrzb8LTNYd/+ThfhTDApvdDi6pr3koRWlJEQoLimBxBMNu5o6ysoJ8FyyBIe7PpOYNak0l4PcE1Eny+0eoHVACDZvAvn378Pzzzxu73fFsqHvvvdeYVeI7WHQajmcQx0ocKsbcWdB8L2nJ5zJBvl/FQMdKAtO0nzNWMiMls1LiRMXGxhozVXLPmI4VDxKWvlcUsx3PMmlbYyVwtARKS4tR7CpBqcUKm82C8r/EwmxkfHErYq+YhQkfp+H1cxwoP+98wUTEjc3B5D+fxoVNYxFaU6fg4Pd4+Z7H8MLGdrj54ftw/9ntwE2NahboQJSgqKgYf/91+qjJYjMOc/el5dKSYuN7oKTUAqvNaiydDzi4EC9MfBSf5g/GzQ/egctOi4EFJYYcN2wKsFiNH4R861oqti6djmev/hBNpv2CSf1Nz6UoG2krp+D8oS/i1JlJeOksGxxWPYTex6esYvWYgG7cUI8frnZNCTQEAo0bNzaW3p1xxhnGrNLNN9+MJUuWYOLEiejYseNxRcDldImJicbla8M884nL++g0ieNUUbxlyxbD4eKMlThWnu3wPShuXkHHSpyoymIp4+YV3oI4Ut7KNE8JeCeQjt2r38fdZzyDlaOewXczrkQLLjM1CRcV5GLa6DDgiwL8ewTw92TP+7imxX8x/92deGVsc8TV5BSL5CRsT0/D/t/exkM3JyPlsYfw0DW9EG1q3+dk/gFsmjcZl45+EWtMPyxUVr/8x46rPsL+1y5Ao/DAysSNsqQv78NVD0/Hwj+7494ZT+G2S3uhyeFaOz59Cg/xB5SH7sa4sJ/w5BNP4LH3V6LLhXfjwSdewMW+fM2lpuDgr4vx3y0rgXNbYcYFz2LB+1eghcmyguw0/PuCSPzfDZ8i7/WzTCWaVAINk4A6SQ3zuWuvlUC9IsCznkaPHm1s5DB58mTMmjXL2PnukUcewbBhw+p0X7mcjtuX8yBfCeWDLI/ZJZazTA7T9ZypEudKZqsYb9u2DStWrEBmZqbxThV1mB0fpnlx9kkcJs5IVZQ2l9GxMusS+zVu6AQi0bzNKEx67gd0u/VadFr0GM5+dhFmX2MaktsCYZ3wCaaOAI6cCRmH4QND4ayJgwRg3e9LsGvbWqDFebjr8Um4/4qeNXOQ+BiDghDUrDnaIAT725yHp+e/h6uaV/Z8v8Xt4WMwLa8Qrg3bsKOoGLEAqhpsJZ73PN6BBRMenY7JV56OlavfweOXNjrc0OkY2n8Q+nQvwa//XYDftsdi6HnnoG37dCQlHwI6soXKQ1pKEn5d/AUQ2gwnjX4K30wf5+YgldUORFDgBMx5dXjlyrRUCTQQAlX93TYQDNpNJaAE/J0AB+utWrUCnaQBAwbgu+++M94r8pd+mZ0Nc9qb/dy+nbNFnLVioOMkjpW3NPO49bt58wqzIyVp2RWQjhXPoqJjJYE2iV2Slhkr83I/z7R5qSDLzEsBRbfG9ZBAeHO0GjUJry2ag1s+ScXiJ/6LH655AoOkq0UFcP3faIT+jy/HXIOZh17B2VLWvT1aHpiB8cOfw8LdKWh35yd47/bBaBdX9YwMsBa/L92FbetK0eua/hhyeg/EHOmFAbsXY/qUJ3Dz1AM445I78dz0q9FF2veIOQdWNgvGw7dtsFU6crIasgHoiX+9fCM6hYdU6SAZzQVY0Pjci3De/5Zg8+9LsXt/ElIzxUss+9tL/XUOZnz6FQ42vwr3T3oI57cPQIC1UmMO9yQVKUnLsPjLUsS2isclEy5Hy7+n7tx6y56alyYuvCMal7ydjzxXDzzyw+e4tXsUnL406aZVb5SAfxLQj7p/Pje1WgkoAS8EOHjnwH3s2LHGzBKdifoaxFHxtX90TsLCwtCsWbNyp0p26xPHyjMuKioyHCs6UbLznzkWp4rOF5cC0rHiwcXUK/aZY1kKKJtX0GkSJ6qimLJ8L4t6NPgRgYAAhEXGYdCZo4H5P6GwnxVLB0Xg/NWlKHXlGx0JKC3GqY8+jo6THsO4Rh8goDgfWXmFwO9PY+SQABS0vx0v3bkSL7/3MRaOPQXxcc0QwZr7v8AD4x/HjF82I/0IJCVw5eejoAQIeO8RXDTryb/feTLLchODwgLkF5biuw9ewD2lpXjhnWsqdJTMVX1LW2BzWL3MkrnXXvH6SNzxXQdc/8BEXNirO657fz6uchUD9iAEZfyI9SKeuhSffLAUawJGYcJN/8QFHcve5cr441NMnfwYnv0lHufd+CBevHsw4qWOxKnJSFq+BF8WxyEqsxnWvxmNyPOkkHEpSrlpBgpQnPMmxkS8W+7YFeVnYszbS9Bx2ihMfmAaBs68GT1jneXlZi2aVgL1jYBu3FDfnqj2RwkoASVQiwTMjpSkK4s5Y5WRkXHEWVXiXNGhMl/p6enGdut8x8p8rhSdIt7z4rI+cazoTJkdKrn3jLmToIYTTKC0BMUF6di1dTGm33kH/ho1B9PGxiBz3v1off0nsF/5HpKnjoItJweG2/TDJLQf/w5S29yBLz+8CT0T4xDhKEJevgXBYSEIlA0gSgqRk52LApfnZgrJ+HHK/Xj+zc+R1OtuTLrzHxjTdAPee24htkedhsvvH4G2XpFYYXcEIYSHsB9Rno5dK9/DXT1uxydWB0LCQxFY6Z4GLuSkZiK/tDeeWj4JaePuQPHjP2LSeU0QE+ypfAVeHX4dJv+wEWkhwXBI/0SsxIUul1+O5lv34I8fNyO4WyzyCvZj05Z0BNrKfjTgToF5ebnIKwqAo9M5uObep/HmuFNEgxGnblqEdx8YgUdWdMQFj8zE1ItjUVhoEinIwKGvH8BJN3yGIOfV+N/OZzDEVBwYFgFbfiZyS4MRbhxUrz9YmPBosh4TOPL7oB53VrumBJSAElAC1SNAJ6W6gbsM0pEyO1NyLzFnrSTNXQG5tM88MyVpxnSq6Ezx2rhxo+GEccaKOsyOlThVjPmul6dj5XlPx0ryZClgTfpbXT71Vz4NO5Z9hH+dPxUbOk/AK/MnondQIMKC85C+4RSc+eqpiInOgy000Jhh6X5yMGZd1AqPrRyH/259BsPCg4xD0XFSR5wSH4+EdVMw4Jbp2BJ1Jd558yYMahMLY8GdxQFnuANOD5DJP76LLxavwmrnKNx28Vic178lYjL3wxm0CfP+8w5mfDcMV91yJyZd27sG7ygFIabZCDw0eyouburRsNvtIjzY6hq4Xp+J60Ln44a0ZEQXlMC0cWW59P4vP8Anu3ZhX6tL8PyLN+OCHs0gC+xEyBESAltxsbF1d4Cxb3cxXJxpMsIuLJ72Oia/vgmJN9yNJ28/E82cYVK1LM7bg52rvsKHXwci+qTuGDq4LcLCipG2ewnemngn5vT4N5bdcTKKQ0mWy/qCEM4Z3tIS/HB/c1z9QTE6P7AQH13XEdGVe4fu7eqdEqgHBNRJqgcPUbugBJSAEqhLBMRZ8dUmOjvcpZCzSeI4SVxRHpcCcsaKDpQ4Up5plvPasGGDEcs7VmIfN/yQNGPOWEVGRpY7TuJAMa4ozaWArKuBBCLQ9KRhuOGx33HjbY/hqtO/wY1PvY/R++Zj+t7v4BjcCs9ZS1GSl4aSokKseWo87s07hPSC/+Cq9jMRVJCB1OwC4Is70X/xJFhdmUhKyYbL8jyueCoR8/91Ebo1C68A9Tp8N/N7rFq6D12uvBND+nRHnN0CREUjNjIUzuwgxJ/aB8OHdkVkBRoqzw6AxRqMiLhGaCT7KXitMBovbh4AxDRG5G9bsLqoCE127UFhEd8fNA25Vv4bNz39OX7ZmoE2V/VDj44noZXlN0y+ezI++GEdUr3q/juz0YArcOWZrZCxLguxIyfg3nvPRcewILgdfwQg79B+bFj2A9YU5KB0/SK8fvMbOHnu9WhTXICsnX9i9cLRaD7VDru9GJe/8QqK/3kPrmz+GbjXZX7aXhzKBg5MehOLLn8WI3iG0t8maEoJ1HsCpr/Yet9X7aASUAJKQAnUQQLy3lJ1nQ1xoMyxOU1Hi/eSJ44VZ6Q8HSqZraIjRccqKSnJmN2SA4JpmzhV5piOVUREhOFEiTNVWUwnjEsBq9vXOvjYTCbxBKESAFbYwlqh/cCLcengN/Hwyq3475cL0ChvIULOfBSz3xiDuKy1eOfq0Xg+BSjMTMcpk77HR1e1gNWyCzOuPhfP/FqK3NwU7Bv6AhY9fDaaRx9eo+aMRZOYipdQrp/1Omb+/D02dL0ST40ZgUGt7DBcVz43XrAhKDgYISGBZfkm631L5iE7dw1++HUvrm4hm3N7qxmEqKZlU01LFs5EdmEeVr30P/x0ZQecFx7990zRKePwxuxzkV9YDEd4nOHIBSTnISM5CXt3N8MFk+/E+NHd4O6PbcPCl6bg9X9/hdUbd2DIeRfh3ldG4TprKKK9OEhACnZt/gmf/99fcKEEzvgwtD//THQGkEvTS4sR6HDg7Od+xP2nWxAWF4fS4cORxXeUSnfgf5cOx9OrSnDyTWPRO8gBu7fuap4SqMcE1Emqxw9Xu6YElIASqM8E6Kzwstt9H76J0+RrTMeKS/vEiWJsTstsFWX27t1ryPKAYDpoYp9nzKWAPOyYzpTMXFWUlnJuulF3N6+gg1RkOEkIsCIqvjkGDhmOsPWb0L1VLn5+sRSn3TkKfZs2Qtb81/H+LicunfohWk47H1PenICHWi3E/1p/h3c3FKHdrbPxZvuvMfq5/2DCey2x4OFz0DrWcxGa6VO99kNMfPptfP79MhwMG3Q3pi0AACAASURBVIpb7roOlw1qg+CqRjf7lmH2lz/hp9K+ePSmfsY23SatpmQ4YhL7YfR1HTDnnc2YM/EM/PKob5+33IN7kJFbiuKcGVj4y304Mz4aIfJeUlAkGjetaE6LM1ZN0Lx1a7iv7HMhISoUDpyC4f1GYPQ5pyA6rOK5nbw9W7Fq7rf4sbQ52rXbgiSXDUGRocYskeEkAbBYbYhMbI1WLQ93uXVrxNFJKilGTFDZu0eB0REI9jjnygRIk0qg3hKo6muk3nZcO6YElIASUAINj4A4LNXpuThUdJg8097yuHkFZ6DkPSpxquSeMZ0qxrt37zbSubm55Y4Vd2UUOyXNg4o5YyVOkzlm2tv98Zux4pzN38MJS0RzdJ8wDb8M24nUFe/g0gIHLmrbGDlr38PNd32GVrd+ghcv6wtL0Bg8e+0c7F45DROefBs7BzyLX24fjq7R/fH8mh64c85EjJi/FE98cT9GtouH13mkgnTs27UNO5KiMPKay3DR8B5IDKnCidn3G2a+9Tzu+s9PyIrui6LS+/DEPypylCwIiT8Fo574Eiuv2YZV/5uI2z7OwCUvfIs7e8mnqBQlRdsxY/z5eOH3C/Hy8ocwMNDmtvQtokljRPmyg7morDQOQlhoOCIqcZA4i7Rz/U9Y9itwwaRHEf3DlXhtw5FKc1L24u0JnfGN5wteKEDKtmIU0//VoAQaKIG/v9UaKADtthJQAvWPwJ9//okvvvgCbdq0wciRI42tr+tfL7VHx4uAOCx0VHwJsikFHShxoiRd0b2nY8UZKrNTxXs6XtyOfefOnUaaM1Zsi46UOFOSZswZK85AiRMlMZ0t5vPiO1W8eOZW06ZNjXOsfOmjuwxnHExvw1gdCI5pjhauHKz+bQFc9sHo7JyBm654GT8ljMeHtw5C4wg7Sse8iFUxrXD7Df+HLxKuwP+eHofOzcJgtYTjgkeewpJ5d+ODDdNxz3kL8PGNr+G5K09H6xjTM0j+Ea++OhvL1+wB0BKRpesx5/Eb8MhvfyKl3MB8ZOxPQhKyYf34aUxY8hZCS7JwKLQteo2/C4NdKzBv7kf4bkg/XNKhvJJbIsAWiLBGbXGKw4bU+GDkZ27F/NcmIvThd/HqGC69K0Gxy4ZGxoTXD5gyoRNOXvIABjkDTa6jm8oqbpbhvUeuxrxXnB7vAElfWlewS9/falM2rMC8BfOx7cxb8OwwJ75a8neZORUUHodz7/8fbutpzuVyu9346LoxeHmNeklmMppuWATUSWpYz1t7qwTqNYHk5GR8/PHHmDlzJg4cOICbb765nr37Ua8fX73pHJfF0bGS5XF0ZCTNZXh0lOgU8eLOfuY0IdC54SwQdfDcLzo3eXl54GwTY3OajpLkMc7nGUEFBYZOtiNts32+B2V2osT5Y8w2H3jgAVx22WW19BxykJm2Eos+3YfU7G/x1G3LUBAxCs9fkozbLxiGiPOfxvcX7cTUh2fh94RxeOvdO3Fu+1jYLSvw2jkPYk7KHrS8bjrmDIhHRDAQktgOieGmIUvyD3j1gY+wN7odTmq7F+lrAISfjLMuvhTjxmfDVd6LXfjpzWl4++3fEXbG5Zhw0xh05WFLwZGIi49BVMlFOCsDiGpWXqHKRIklCPlR/TFmQKwX2RSEDOyPk63uM0leBI/Mih2Am155H2Myco3Fi0hbhvdfmoYZ84EBE6/HVZf1QtnbUE7ENG58OH2kGuAQ9m9NQtrWfhg/9UzEp/3qTcjIs9odiG/XDd26mUW43C4UPzsDuOGdBiXQYAmYvnEaLAPtuBJQAn5OgIPMRYsW4d1338Wvv/5qzCDde++9GD58eA1/GfdzIA3IfDoBXAJHh8DT4RBnRPJ9kRGHRWKpK/fe4opkaBedIl6005fYFxnqMjs4sqmEzB6xTJwhSfO9LV7Mp8MkdtFG9olboHPr9loLORlIW/UDPiuMQfxlD+PdG3vAHhyFxhufwpVLf4Zl240YNKsNBl01Gi2f/BIv/HMZXrMEIADp2LF8I5IKi7A2bjuuu3YgujUOc980YN1HuOPpNzD7z7a4+46TkL12GVZwKwJ7BBq3PRmnuG2AF4Gkr2LAOZnw+Jbo2K0Xern5NjGI5MZz1QkBVljDmqJFnPf1c6HNGiGCfVnxGs57+DOkZhUAQx/AjNuG/L0Rham9Qz9NwWNvzcOaHVmmXACudOzdvgvM/eXz/2D38o+M94nchGK6Yvhl1+LWS7sjqrwgHK36jMCE1iWITnCiIK284IhEbup+fHBbfywxTuk1FZfmYd9fJbrczoREkw2PgDpJDe+Za48bKAEOivhLNAd0THOwxGU21XnpvS6i47k57733HubOnWsMlseNG4dzzz0XJ510krGcSH7Br4u2+5NNHJh7cxC85Xk6I5SpaZ6noyO6GPOzzHIZ8DMWZ6Q20vzsmJ0R/s14u/fM5453/NtivjgntRlTLx0jmR1i2nxvzjenRUY+d2QlvLiUMCEhQYqOMs7GwV0/4u3Hv0B+WCRGXzAO/fpGAjy0dCN3VbPAmtoB5334EC7rHIcxPUYjk7YYra7Cm2Mfw+cpiTj7/D5oFh5sXsgHYB1mvTQd3xzohFsevBoXd9mJ12Ydpbke1TN2LMfXb92Lab+4F5S48pGxZxPgykf60sm4bPAHKNuHgRxzsfcvGOchrfvPtTj7yxBYM7ZiaZNr8f5Za3H7jEcwtcspuHtES8TK5g2H1RembMdfq3/DkuzTcdsdl2FE1yZletNXYubr72D2d0DPs6/B5RedhjJ/Lgsb57+P96bPwpLluYhrNxjJbk6SA86YBDhjyhoocO+G211gaBTOuPZJ3OA2k8RntAef3H41pv5V9lTcKumNEmggBNRJaiAPWrvZcAlwEMkXx/kuA50k/nLMgREHTBwYcSkPf0Xmsh4OqPwl8B2NTz75BLNnz8bmzZvRt29fjB07Fj179jR+EedA0h+D/LJvdj7EUagqz1wu6ZrW9axnnhWpyCnxdExETgbicm+Wk88inQ9+Jj0dDrNTwrQ4CIz5+Q0ODvbqjLC8KsfEm4w5T2wS54Kx+fJ0QLzJ+SLjrV5VeXXqs12aB5RkAUUB2PbLbDz39MdYn7kPmzYlw9Y0BvFRnru4WWCxNkbngd2M3duaDjTv4VaMbxx2WBGGFm0aI9xh89iyuwl6/+Nf+K8lAR3bNUFs1kEPJ+royYTEtcbpFz+IuMFmXUXIOrAGc/71KzaFJ2DUva/huq5SXoqS4t34/K7l2LYeaDLketwzugWCbAFAYmcMiM9A4sB9iGgTj0r3WghJRLsuvdF/UFsYR8IeLMLyWeFwIACJbbqg96BBaG00mQrnrh/xTSDQqkdnnDGsHxqLKdWMrY4gNOs6CIMGmStyud1m/BHBw2XN+ZpWAg2LgH+OIhrWM9LeKoEaE+BAedeuXcZhm3xXgQNdc+AAju800OFo0qSJ4TBxcFaXAwfYP//8M95//30sXrwYjRo1wsSJEzFkyBC0bNnSp+V1HJh7OgFyLzHZmdO+Oh1SR2Jf60l7ZkeiOmlvjoi5Pp+pOBx8xpKWuKo8OiRmB0IcEMmTmPnmtLd7cx5l2XZFlzcnoyJZc35t1KMODb4QsAGWYMBmR1yHQbhiYhy2bf0e//1tBzba26DxERNURXAVfIlHRm7Da0eoP4QNqVnIOyJfMiLQ4rQ+aCG3HivUJPtoYrszBi27DoPsim3ocqVjz29r8dbBEISEj8CF1w7DsMMzNWUbN2zE+sO+YET7vhhy5slwOuS7tBH6x1ewK8TRGAogND4ajZo18r7z31Hq1upKoKETUCfJzz8BRSUlyHcVo6i4BDaeGG+3wmaVL2Y/75yaf1QEOOimg8QNDLgsyVvgwFpe8uaAmoEzSxxs1sWwY8cOTJkyxXCSeCaNnC3DfG7WILMwZgdF0hKTi8yKSGx2JqqTlvrkKGmyMzse5nvmy0C+IhlPB4L3VeVVVU5HxNy22CC2MaZDUJGMWb6yNOuLc1IdXXXxs6Y2VYNAAIcSTsBqQVjiyRgQG4dE25+YBn4ewv8+G6hcpQVWWyecc8P18FzlBfyBd39Zh32VrREr13N0idxdyzDv4+lYkNoOo6+9E8PLpmm8KHUhM3kdvnnjLaxCJDpdPh5Dyh0kL+J+lJWXfhCfPHwBNrq9p8XlgznYvr4Exbrazo+epppa2wTUSaptosdJX56rCMlZ+TiYlYecgiKUcPlUQACcDhsSwoMRFxaEILs+3uP0OOpkMwcPHgR3e6vIQTIbTceAs0nc4YoX36eoi2Hp0qXG1t7btm0D3/vgQJzvJG3dutVwPmRgLrEM6D3v6TTwog7GZkdE7msSs05FbTLf047KZCuS96bDlzydFamLn+j6YhNn3Eyzbvl5yN29A2sBjy2spb9cbtcKfc4/HyMkqzwOxZIgB2zYhqRkF1w8o9a063e52BGJfVj83tM4uORtRLgdk5SFfWvXYSsOwT5/Gh7Z+x1iD++34MrYh20b/8D2vGjszAQw0bujlHtoJ36Ydi9e/jENjnPvwzP/6FnJ4bNHGFZrGVkbvsV/P/4U3/6+H6k7N2BDcjRaOOMQ6bZRRfWaswc5ccrQK3BFJ496pfsxd/MirEtVL8mDjN42IAI6ivbDh52V78KOlCzsS89FbmEROJskwWYJQGpuAdJyg9EmLgLOQH3EwqYhxZw1oYPEWSJfAx0lnsvCmaS6+n7SaaedhltuucV4D+mPP/4w3rE6/fTTMWzYMOOlc3EWJPbmaEiZxN4cFXNZRWmzbrOMr7xVTgnUWwLcbTA3B+kA4mvcyTRk5xSjxOcxehiade6HYWd2QVOPjREwpnIj0rYuwdK13+KzFaMxvHUrk3Ah0vesxmfPPYp/f7cJWQPuxn/uuwp9WxoHIpnkaiPZBIPPHoAubRIObwZh1pmGzKwsuII6oWv/s4HSufhs61LYOp2JkWMuxGlHsSmhLciJDgPHYqzb+1dl7yTte7XsnaQtH96PCSsjYbcGoN8/X8fVvaIR6pPjau6DppWA/xHQEbSfPbOcgjIHiU4Sl9l5hqKSUmTkFaKgqNjYKah9QgSCdUbJE1O9v+esEM9M4TKw6gQ6VTywksvYOKNU10KHDh0QGxsLOkvff/895s2bZ8wi8XDMUaNGGZs30HYNSkAJnEACIXFoOexWvPvuhQgOa4bubku5aFcRigoXYvLVV+PDI8zcj5XpOcg/Ir+KjEZdMeDcC3HF2J5IdJtJqqIegNxDfdB3yyZkRJftg52190/8OGsyZq8pQl56CvZvz0OPW/6DEf364dxTq3b7tu47iJKSjlU3fFgi8rRxePCZEQg7qTs6NQ7zcgBtXtkPXhHt0GNQO3TudAq69huO/dYW6Ny1A+Ir/aouRGFBGlKTgfxDO/HT1Dtw9dwgFJS4sL/VBFwRMRNfP3s1kt4xm8vldlnYUHIpbr10Of49ez5WtXsU5xx8AW891gMd3r8BAxqFotJmzeo0rQT8lEBAaXVHUX7a0fpgNoe7Ow9lYX1SOrIL/j4qr6K+hQXZ0bFRJFrEGPvkVCSm+fWQAJegcbkd35OpbqAT0qJFC2P77OrWPZ7ySUlJWLNmDRYsWIAff/zRWDLHHe7oLPXo0aPO2388WWlbSqBOECguRP7OX/HBos0+mdN6wMXo0zoMwZX9nJu7Cyt/XoUNmVE4tU8XnNQ00v1MJZ9achfKT9+LTcvnYfku5tvhjGqFnmcNQJsKVyFz5iUD676Zg98OAqXthuKq05vBfrTvB+ftw5+/rcJfW3MR16UHup7aGrHVnsEpQs6hHfjzpx+x1nxeUmAonK264TTXYvy01b3/xh03LYnvhWEtd2LRioOI6jYKnXO+xdItTdFrTB+0Cg304sx50aNZSsCPCaiT5EcPj0vr1u9Pw46UbOMdpKpMt1oC0CTSie7NY4/+y7qqxrS8ThFYtWoVsrKyqj2TxE5ER0cbThJnZ+p64G883JxixYoVmD9/PpYtWwbafcYZZ2DkyJE49dRTfdrtrq73U+1TAkpACSgBJaAEji8B62OPPfbY8W1SW6spgUPZ+diTlmO8h+SLDq604jtKcWHBxq53vtRRmfpBgO8W1WS5HXvP95H4XlJdXG7n+XS4GQFtbd++PTp27IhmzZoZ72Jxi3A6Tnw3KzExUWeVPMHpvRJQAkpACSgBJVApgcomsSutqIXHnwCX2OUUcqsf3wNfenUV/72xg+81VdKfCXDJHB2lmiy3o3PEM3H8KXBXOTpKXCbYrVs34/wkLsHjrnd8P4uOkgYloASUgBJQAkpACfhKQJ0kX0nVAblAmxW8vG3YUJF5XFbMZXcaGhYBLjnj9tbc/rs6rx3SQQoNDfU7J0meLu3v3Lkz2rRpA+56x5mkhIQjTrIUcY2VgBJQAkpACSgBJeCVgDpJXrHUzcxoZyAigx3IzCs0dq6rykqem8Sd7UIc+pirYlXfyjkTxNkkLrnz5Zwk9p9L1+hchYeH19nDZH19Tlwy2LVrV1/FVU4JKIF6RoA/DtX1s8E8bZQftOq63fXso1JpdzyfUaXCWlgtAsJW4ooqV1VeUb3ayLfUhhLVcXwI0NmJCgn0+f2iILsVsaFBxuzT8bFQW6lLBOLi4sCLS9GqCvxPmc4R5YODPQ8Zqaq2lisBJaAE6g4BDqp47htnkpmuq4HLoV0uV7mNvBe7ma7MdpFj7G+B/eLFPvKqy4H28RkdC85kwM+or7pF/lgzk+dDu5g2Bykzx+Zyc9pXGdbxbEf0SL6vuijnzW7Rx7iqcmmLclWPnsyaNX1CCXBmKD4sGOl5hdibnlPpu0YOq8WQTYzQAe8JfWgnsHEut2vSpIlhQUpKinHOhnzhmM2iExUWFobGjRsb5yPxYFQNSkAJKAF/JsCBLQeTXIJb0Q9FHARRhj8SUUbu2W+7vZqHLdUAlhz2LQdTc9af378cODOPtlc0q0QZ9pGrBijrb4H/F+Xn5xu214Uf5vg54MXnbmYuS9b5/2ltBz5Drvbgc+blS8jJyTGe+bFmRhb8fPHzKO8o85kxn0z4t8Iy/t2QF9PCjXKsSznmCVORlXGIyPDzK39vEpMF5fg3Qk6ih/nU4+0zTxnq5EU9Zl3ClnaLXZUxl7bVSRJyfhKHB9vRKjYMpShFUkaecWis2XS+feSwWUHnqHVsGMKC/OsFfHNfNH30BLjsrGnTpnA6ncjMzAS/YPmFwy8Afsnwi58OEg9gZcwvOg1KQAkoAX8nwMEcv+u8DZSkb/we5ICKgysOvBh4z/zK6kn9o4nZBgdr/A7md7HZFtrN72LmVRQ42KOc2F2RXF3NZ9+kn3XBRn5eOCAnd/l/UJ4JnQR+Rmo7UCeffXV0U7Y68jWxmZ8t/h2QhzgSZEFGdOpYRka0g3lSRllhxrEG0/w7oox8XuncsR518OB6BvKVct7L3x5lcnNzDXl+zikjl7cfB9gO6/DvilyphzZIYJqfOZZX9ncjfTDkpLLG/kGAH64YZyBslgiEBzqQmltgbAleUlpqbNDAd5D47lJCeLCxNM8/eqVWHksC/FLixfOP+KXELxF+CfBLjk4UL/lP4VjaobqVgBJQAseLAP+vlIEcB1b8zmOeXLznJUEGXyxnvgzgpZ7Is5yBgzAJUkYd4vCYB78slzKpzwEdg9jI9ijHeswT/awnQWyXe89Y7GDMIPLSJvMqkqE9VQ0czbZ49k/0ik3mcmlX6tMesVHkPctYLnYLC5GVmDKsx3Jpv6I6Isu6njLmuuTAcupkmjGfh1lG0oxZTnmmpQ/mPJGVNhlLmnJ0kqU+ZcU+kWGe2ME8/n9tLmObvOcladahzTUJbIvOgXAw6+DYgRcdFHF22CadKdYRZ4czhKzPH17pqFCGebxkRo7OFvP5HjRtZZoX8/k5ZH/YFgN/5GUe+8V22B7vyU8C/34oTx2egXmsJ3wl9pTjPWWph/pog84keaNUx/P44CJDAhEe5EB2oQvZ+S4Ul/CPwoLQQBtCA+3g0jwNSsBMgF9O8gVlzte0ElACSqC+EZCBrQyOOKCSwav8Us2BHAdFjGVgKYMk8uD3pQy8KMP61CuxuQ22w/+bpZz1OWhkHgdcHPxxsMdyBspzoCf3bEfqMp/tsQ6D5LNdllGnZ6D91CH9ZR2pJzHreOoVfWyL76V6BurlxXLWpS7WYZ70T9plXWHDNDnznkw5sC0feHoMcClLGZYz5sV6vNhX0ePNNqlD2yhL21ifttFW0U37mS8yElPGrINy4oiQJcspywE+dZo/O+w373mRB/vIIP/PCheWUY/0iTYKF2mDuiknjOSeOsQm1qM860rfxHb2jfXl2dAmsd0wyod/WJ/9pC7hKNVYJozZP2mfbBhoB2VYl3JkyD4wUJZp2s1AGbKioyU8qYf1OQPFcrGds1PCS+pSP2UlUJ52M7AeyyWIXsqwjtgg5eaY5eQtz9qQNwto2r8IWCwBhqPUONKJZtGhaBwRYtyrg+Rfz1GtVQJKQAkogdolwAEOB5QcjDEtgyUOgngxMJ8DKg7wPO8pL4M5GWAbQocdDQ6kWJ8Xy6mDAzCpJ8uNWIdlbJOy0g7tksEf82WwzEEe71nuWYcyYrvYIjHboB2sRxvM/WU96mRfKSMDSqlLnZ55UkY9rMNlT2I/yygv9omtzJf+CxPpi9yLXuab9clAl3qZ9rRf6plj0U3W5oEx09Ifswz1SpD2xAbG5MQgbfOeaV7ky4ty1M/6LJc8uWeZyLPPZCNtUDflpR3mk6HUYbnkUR8v6Zt8LljX/BlgmjKSx7ZZj/03t2t0rJJ/KCu62RYvz0CHhg6S2dGg7ewT25XAzzXr0wb2nzK8p1PEwLZoozhIzGN90csyysjfB+tLO+yn5Isu+fyZ842GDv/DfDpt/JGCbZptFTm2x35QF4PYpjNJQkhjJaAElIASUAJKoF4Q4ECLFwd1MjPAQZBc8gs1B138lZsyMhDjAIkDOpHlIIuzLBxcyWCSTgPr8J6DO7YjAzAOtGTAR5jM5/lzkmceBFIn26UeDiR5z3Z5T/20U2RkcC0DOHlQlGU/WI91ZDDKdmSgSj1sh7ZRhhf1Uob1Kgos5yCcOjnIZB+YRz3UJzazXGYLaAsvBtomPMiALJnn2SZ18hJWwoFy1EW+noF6xAYu7RLbmMd+017KkBvrU4Z6pX2+pyvPTfSwDm3kPeXYP+plzHvaSJuYFl20S/rDugysz0B9Yjvrmp+HIWD6R9phFvXRbsZckiZ9M4kbSWnXzJa28fPJdlmvqkB5YUZ5PkfmmQPz+RmSYK5DG+RzKuXsJ2WkfabJi3KiW8qkjsS0hRxZTj3kIM+BOmifsGfblKE8PzvCXXRJTPnKAp8NP6fUR5Yir05SZdS0TAkoASWgBJSAEvA7AhyIcWDFwZMMeCSWQVpVnRI58wCQOjh446CKAzLesx0OrijP9njJgJxtSJ60x0EfB3RiD50AGQRSxqxHZNgm02yXl2dgHss5gJTAe7OtUk8GmZSjDNv2FmgHL/aTThB1MTDmAJx1pT51s1/sK8vFoWJ9lrFNaUfsYiyBcqxn5iL6xW6RlVjso26xTXSzjIEx86hXHDfmUyf7xUvsEr2MKSt9YX2m+YxZj2WSR/3UwTIGykkQveTCILLUIfaJLGPRyTTbEIfW/EzN8kxTjze23vR71jXf0362z8867eY9Y+EqstQrDNgv2sn2zY458+UzQB3CmjNrfA4MbIuXBOrlvZkN9fASO8Q+8qYc89k+Y3EIJd/TbmnHWyx9Yl3qoY1MU8ffT9NbTc1TAkpACSgBJaAElIAfEvAciNWkC9RhHnDJ4JODKKZlAMfBGgeWvDjIYiyOhLQrdTn45WCUepnHgaAMyEWWetm2OVBWdHjm815sMZcxLbZKXzz1et5LfeazPdppZsBy88CdZeyD9J99YT1ykDY960sbnnFFtnjKme8r0007aJsMes31yIvBzJRpXnxGtJ+6aZM4SSzjIJ338pxl0E5Z6qQM22O7LDP3iWUiY7bFnKY85ajfzNksI2nKmfVLfnVi2ih9oj5+lkUv8+XzyXb4jDlzQxne0xmWHxFYh4H5/OyL7eTCMmmH5cwjI3OgjDwnpulUUQfbEGbUwVky3lOWcnwWzGeal9jJmGWMKwusw37RJl7sL+sY9SurqGVKQAkoASWgBJSAEmiIBDhg4iDLM8jgjuUcYHEwy0GhOAkcXMlAjoM8GaQxZl1eHOSxPtMc4InTxLbYJvVKYFrqsY7oY7nIUR+D6JYy3lOGdXh56hY5o3IF/7B/0o6IcCBJW3hRJ2XYlgw22X/WYf9ZzrbFDtFxrGOxmTaKHWxT+NEu4SZ5YiefCZ8p6zIwloG5570wJgPK8Z6B8ubnSt0ia5YzhA8/S5bTOaDt1EdnRPiJXG3H7Cufm/CS9sTJM/eBDhLzyYaX8BOb2EfWF26Sz5jtMLCMz0M4MY/1WM6YOmmLyJl1sZxllGXszW46uCyjftb1tNEwwvQP5eRvVz6/7IPB3iSnSSWgBJSAElACSkAJNAgCHHBxECUDL3baPCDjPQdYHERx0MXANAdhMhDkYI33fMeHg1pezONFWQYZzLEOB5iMZeDGgRjT5naZlkGgtEs5plmX9kpgmhfzGVhPdDHt2R7lpD/Sb7GT9aU9DhalLQ5oRS/rsCwrK8sYJLOMtnEgzX6wDmWpkzED86hXdDMWGwwBH/8RHawr/CqrKmxoI5nw2TBQD7nwot0SaCeDJzPmURfbZBmDPDPq5fOXPMYiK3milzzIisHcrpFx2C7qopPE5Zpsg6xZh304VoFc2JY8H9pPW+UzIEstxXa+syP2SB3G0m/P583nRW5SLs+CbUg9yrDfrMty5pvbYF0G2kBZyrCctosNlKHNot/s5FbGjn1nH816mKZ9R/5EUpkmLVMCSkAJKAEloASUQB0mwMESBzky6BFTee85RIhLQwAABKRJREFUwOaASgZxHCyxLi8O6lgmA0MOXmUQxjIuAeI9A2P+wk55tsE6MlBjGevyjDq+gM80B3bMpyzv2a60LfUl5qBP5Mw6mcdL2mcZA+XZH6nPgZ7YShlezGO7EkQP76UvtJdn61GetoleyrD/rMN+sFz6yDSDDH454GVdyrK+DJTFPsYSKGO2w5xvTnMgTb6yXbm3Oma9tIl2MI99pq1MczMKDrJ5LxdtpW4+S5ZJf9g+ZRjYB8rxMuexDV4iy3K2w36TBwN18+KAnHVpu9jKmGXsHx0oDvClnLNytEV0MpYg9eWesbc8c7lnmrrNfTXrkH6xD9RLu5kmIwmSL/2iLubJZ4zy7BfLxXbKsH/yTMiJaXmXj+2Y9Uia7fJzx0t0iR2MqYeBZd7K2SYvc2Bb8uwknzZTTp0kIaKxElACSkAJKAElUC8IyCCLgysJHPTwXgZPlGEeB2ccFHE2iEHuOVBlPuWYR1mm6XSwjIG6mMfBmwxO2YbMBFBGdLA+gzgXnvdGoekfDshlsMY6HGQyZmCbbEf6x5gDPXEiqJv3tFVmLVhHZGSQyzLm82JgPQ405Z4xHQYO1KmbgW1xNoG2sJwMWCa2Mo+2im62yTK2KSxYl/nmZ2Huj9jCmHJyL/XZBi/WER2G0OE+mDnRfjpFbJ/948U8smF9BuqikyJ9YH0pM+tlnrmMz5ztm/MozzyWeXLh50Y+O9IHsZ/cqV+cWrGLelgmchJX1n8yY3lNAxnRFmHAe3HyvOlke+wX5Znm54U2sx4Dy9gvCZ7PhPksp5zYTabmzzN1MY9/p8JA9Jljlond5nymqUO4eZZ53lMuoFR64Fmq90pACSgBJaAElIASaIAEODTioJAzKhy8cVAnAywZxHliMTtJnmW8Z33KcABHHbz3RacxWKvGgNebXsnz1j4HtHTIYmJivJldnic6KrJH+l9RORVRprLy8saOQULsp+rKBtFmRrVhhrR7IvptdlQq6ktlTkVFdXzJJ0dhWRFvlpMP2VQkw3LKVSbjiz01kdGZpJpQ0zpKQAkoASWgBJRAvScgAzhfBrgVDfIEEnWYfwGvDZ2i2xx708s89oWOH4PMznAQzRkWmUUz6/FMe9Nrlqmq/5T1RcasszbTVdnPtihjfka10b4v7dZGO950yEyWtzLm0TbO+vFHgNoOfNZVPe+qysXG2n4mvvZVnSRfSamcElACSkAJKAEl0CAIcPDIcCIHuMcCNB0lLh1kv5jm8jIui/PFSToW9qjOY0uAyz411JyALrerOTutqQSUgBJQAkpACdRTArJciL9ii9NUH7pKx4gzSAzsm8wq1Ye+aR+UQG0SUCepNmmqLiWgBJSAElACSkAJKAEloAT8nsDf2774fVe0A0pACSgBJaAElIASUAJKQAkogaMnoE7S0TNUDUpACSgBJaAElIASUAJKQAnUIwLqJNWjh6ldUQJKQAkoASWgBJSAElACSuDoCaiTdPQMVYMSUAJKQAkoASWgBJSAElAC9YiAOkn16GFqV5SAElACSkAJKAEloASUgBI4egLqJB09Q9WgBJSAElACSkAJKAEloASUQD0i8P+AyBAJdXBlCgAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 箱线图\n",
    "\n",
    " 箱线图并不显示数据分布的形状，但它可以让我们更好地了解数据分布的中心位置和扩散程度以及可能存在的任何潜在异常值。 箱线图和直方图通常互为补充，有助于我们更好地了解数据,下面我们解释一下箱线图，箱线图是描述数据值与数据数量之间的关系:\n",
    " \n",
    "![image.png](attachment:image.png)\n",
    " \n",
    " 我们将一组样本数据进行排序，然后我们观察数据值与数据数量之间的关系，我们就可以画出所谓的箱线图，如上图所示Q1表示第一四分位数，Q3表示第三四分位数，IQR表示四分位数间距，任何落在上边缘和下边缘范围之外的数据我们称为潜在异常值，即 异常值<(Q1-1.5×IQR) 或者 异常值>(Q3+1.5×IQR). \n",
    " \n",
    " 最大值的上限位置位于Q3+1.5×IQR。最大值上限位置以外的数据，我们称为潜在的异常值。\n",
    "\n",
    "下面我们查看房屋销售价格的箱线图,从图中我们可以看到数据的最小值，最大值，第一四分位数(Q1)，第三四分位数(Q3)，中位数(median)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "responsive": true,
        "showLink": true
       },
       "data": [
        {
         "boxpoints": false,
         "line": {
          "width": 1.3
         },
         "marker": {
          "color": "rgba(255, 153, 51, 1.0)"
         },
         "name": "SalePrice",
         "orientation": "v",
         "type": "box",
         "uid": "c18b692f-cd60-4ab1-a1d9-393d87c0778b",
         "y": [
          208500,
          181500,
          223500,
          140000,
          250000,
          143000,
          307000,
          200000,
          129900,
          118000,
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   ],
   "source": [
    "df['SalePrice'].iplot(kind='box', title='销售价格箱线图')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分组查看箱线图和直方图\n",
    "\n",
    "## 对变量CentralAir进行分组查看它们对销售价格的影响\n",
    "\n",
    "我们想通过分组的方式查看当分类型变量取不同的值时它对房屋销售价格有什么影响，如带有空调的房子和不带空调的房子，它们的销售价格会有差异吗？房屋的车库面积(1车位，2车位...)对房屋销售价格会有什么影响吗？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~tongzheming/146.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<chart_studio.tools.PlotlyDisplay object>"
      ]
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "# plotly.tools.set_credentials_file(username='tongzheming', api_key='6nJu1wfwzWOvyFZKDFzK')\n",
    "\n",
    "trace0 = go.Box(\n",
    "    y=df.loc[df['CentralAir'] == 'Y']['SalePrice'],\n",
    "    name = '带空调',\n",
    "    marker = dict(color = 'rgb(214, 12, 140)',\n",
    "    )\n",
    ")\n",
    "trace1 = go.Box(\n",
    "    y=df.loc[df['CentralAir'] == 'N']['SalePrice'],\n",
    "    name = '不带空调',\n",
    "    marker = dict(color = 'rgb(0, 128, 128)',\n",
    "    )\n",
    ")\n",
    "data = [trace0, trace1]\n",
    "layout = go.Layout(\n",
    "    title = \"销售价格箱体图\"\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data,layout=layout)\n",
    "py.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面的箱线图中我们可以看见带空调的房屋销售价格的中位数(median)比不带空调的价格要高\n",
    "\n",
    "带中央空调与不带中央空调的房子它们的房屋销售价格是否存在差异？ 从下面的直方图中我可以发现带空调的房屋销售价格与不带空调的房屋销售价格相比整体往右边偏移，说明有空调的房屋销售价格的均值要高于不带空调的价格。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~tongzheming/148.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<chart_studio.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace0 = go.Histogram(\n",
    "    x=df.loc[df['CentralAir'] == 'Y']['SalePrice'], name='带中央空调',\n",
    "    opacity=0.75\n",
    ")\n",
    "trace1 = go.Histogram(\n",
    "    x=df.loc[df['CentralAir'] == 'N']['SalePrice'], name='不带中央空调',\n",
    "    opacity=0.75\n",
    ")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "layout = go.Layout(barmode='overlay', title='中央空调与销售价格的关系直方图')\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同样我们可以使用pandas的groupby方法对相关变量进行分组统计: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CentralAir</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>95.0</td>\n",
       "      <td>105264.073684</td>\n",
       "      <td>40671.273961</td>\n",
       "      <td>34900.0</td>\n",
       "      <td>82000.0</td>\n",
       "      <td>98000.0</td>\n",
       "      <td>128500.0</td>\n",
       "      <td>265979.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>1365.0</td>\n",
       "      <td>186186.709890</td>\n",
       "      <td>78805.206820</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>134800.0</td>\n",
       "      <td>168000.0</td>\n",
       "      <td>219210.0</td>\n",
       "      <td>755000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count           mean           std      min       25%       50%  \\\n",
       "CentralAir                                                                     \n",
       "N             95.0  105264.073684  40671.273961  34900.0   82000.0   98000.0   \n",
       "Y           1365.0  186186.709890  78805.206820  52000.0  134800.0  168000.0   \n",
       "\n",
       "                 75%       max  \n",
       "CentralAir                      \n",
       "N           128500.0  265979.0  \n",
       "Y           219210.0  755000.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('CentralAir')['SalePrice'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们看到有空调的房屋销售价格的均值(mean)明显高于不带空调的房屋销售价格。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对车库面积进行分组查看它们对销售价格的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~tongzheming/150.embed\" height=\"525px\" width=\"100%\"></iframe>"
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   "source": [
    "trace0 = go.Box(\n",
    "    y=df.loc[df['GarageCars'] == 0]['SalePrice'],\n",
    "    name = 'no garage',\n",
    "    marker = dict(\n",
    "        color = 'rgb(214, 12, 140)',\n",
    "    )\n",
    ")\n",
    "trace1 = go.Box(\n",
    "    y=df.loc[df['GarageCars'] == 1]['SalePrice'],\n",
    "    name = '1-car garage',\n",
    "    marker = dict(\n",
    "        color = 'rgb(0, 128, 128)',\n",
    "    )\n",
    ")\n",
    "trace2 = go.Box(\n",
    "    y=df.loc[df['GarageCars'] == 2]['SalePrice'],\n",
    "    name = '2-cars garage',\n",
    "    marker = dict(\n",
    "        color = 'rgb(12, 102, 14)',\n",
    "    )\n",
    ")\n",
    "trace3 = go.Box(\n",
    "    y=df.loc[df['GarageCars'] == 3]['SalePrice'],\n",
    "    name = '3-cars garage',\n",
    "    marker = dict(\n",
    "        color = 'rgb(10, 0, 100)',\n",
    "    )\n",
    ")\n",
    "trace4 = go.Box(\n",
    "    y=df.loc[df['GarageCars'] == 4]['SalePrice'],\n",
    "    name = '4-cars garage',\n",
    "    marker = dict(\n",
    "        color = 'rgb(100, 0, 10)',\n",
    "    )\n",
    ")\n",
    "data = [trace0, trace1, trace2, trace3, trace4]\n",
    "layout = go.Layout(\n",
    "    title = \"车库大小与销售价格箱体图\"\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data,layout=layout)\n",
    "py.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们看到3车位的房屋销售价格的中位数最高其次是4车位，2车位的，不带车库房屋的销售价格中位数最低。从一般常识来说车库面积越大房屋价格也应该越高,可是从数据的分析中我们发现3车位的价格要高于4车位的价格。这可能和家庭的人口结构有关，所以有时候不能完全相信常识。\n",
    "\n",
    "## 不带车库的房屋销售价格的直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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   "source": [
    "df.loc[df['GarageCars'] == 0]['SalePrice'].iplot(\n",
    "    kind='hist',\n",
    "    bins=50,\n",
    "    xTitle='price',\n",
    "    linecolor='black',\n",
    "    yTitle='count',\n",
    "    title='不带车库的房屋销售价格直方图')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 带1个车位的房屋销售价格直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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    "## 带2个车位的房屋销售价格直方图 "
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    "## 带3个车位的房屋销售价格直方图 "
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     "metadata": {},
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   "source": [
    "df.loc[df['GarageCars'] == 3]['SalePrice'].iplot(\n",
    "    kind='hist',\n",
    "    bins=50,\n",
    "    xTitle='price',\n",
    "    linecolor='black',\n",
    "    yTitle='count',\n",
    "    title='房屋销售价格直方图-带3个车位的车库')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 带4个车位的房屋销售价格直方图 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
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       "\n",
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   ],
   "source": [
    "df.loc[df['GarageCars'] == 4]['SalePrice'].iplot(\n",
    "    kind='hist',\n",
    "    bins=10,\n",
    "    xTitle='price',\n",
    "    linecolor='black',\n",
    "    yTitle='count',\n",
    "    title='Histogram of Sale Price of houses with 4-car garage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 频率分析 \n",
    "\n",
    "频率会告诉我们事情发生的频率。 频率表为我们提供了数据的快照，以便我们能从中找出数据中潜在的模式。\n",
    "\n",
    "## Overall Quality 频率表\n",
    "\n",
    "OverallQual变量表示“整体材料和成品质量”，它的值取自1-10中任意一个数，下面我们查看不同成品质量值所占的比重:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     0.271918\n",
       "6     0.256164\n",
       "7     0.218493\n",
       "8     0.115068\n",
       "4     0.079452\n",
       "9     0.029452\n",
       "3     0.013699\n",
       "10    0.012329\n",
       "2     0.002055\n",
       "1     0.001370\n",
       "Name: OverallQual, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.OverallQual.value_counts()\n",
    "x/x.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GarageCars(车库面积) 频率表\n",
    "\n",
    "GarageCars变量表示车库中的车位数，它的值取自0-4中任意一个数，下面我们查看不同车位数所占的比重:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.564384\n",
       "1    0.252740\n",
       "3    0.123973\n",
       "0    0.055479\n",
       "4    0.003425\n",
       "Name: GarageCars, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.GarageCars.value_counts()\n",
    "x/x.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 是否带空调 频率表\n",
    "\n",
    "CentralAir变量房屋是否带有中央空调，它的值取自Y,N中任意一个数，下面我们查看Y和N所占的比重:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    0.934932\n",
       "N    0.065068\n",
       "Name: CentralAir, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.CentralAir.value_counts()\n",
    "x/x.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数值型变量的整体摘要 \n",
    "\n",
    "我们可以通过describe()方法对数值型变量进行整体性摘要，下面我们获取销售价格的整体摘要:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1460.000000\n",
       "mean     180921.195890\n",
       "std       79442.502883\n",
       "min       34900.000000\n",
       "25%      129975.000000\n",
       "50%      163000.000000\n",
       "75%      214000.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.SalePrice.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们也可以单独计算销售价格的各个单项的统计摘要:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "销售价格均值, - Pandas方法:  180921.19589041095\n",
      "销售价格均值, - Numpy方法:  180921.19589041095\n",
      "价格中位数: - Pandas方法 163000.0\n",
      "50th 分位数,等同于中位数 - Numpy方法:  163000.0\n",
      "75th 分位数 - Numpy方法::  214000.0\n",
      "75th 分位数 - Pandas方法:  214000.0\n"
     ]
    }
   ],
   "source": [
    "print(\"销售价格均值, - Pandas方法: \", df.SalePrice.mean())\n",
    "print(\"销售价格均值, - Numpy方法: \", np.mean(df.SalePrice))\n",
    "print(\"价格中位数: - Pandas方法\", df.SalePrice.median())\n",
    "print(\"50th 分位数,等同于中位数 - Numpy方法: \", np.percentile(df.SalePrice, 50))\n",
    "print(\"75th 分位数 - Numpy方法:: \", np.percentile(df.SalePrice, 75))\n",
    "print(\"75th 分位数 - Pandas方法: \", df.SalePrice.quantile(0.75))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们使用2种方法来计算IQR所占比例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SalePrice的IQR(Q3-Q1)所占比例:  0.5020547945205479\n",
      "SalePrice的IQR(Q3-Q1)所占比例: 0.5020547945205479\n"
     ]
    }
   ],
   "source": [
    "print('SalePrice的IQR(Q3-Q1)所占比例: ', np.mean((df.SalePrice >= 129975) & (df.SalePrice <= 214000)))\n",
    "\n",
    "print(\"SalePrice的IQR(Q3-Q1)所占比例:\",len(df[(df.SalePrice>=129975)&(df.SalePrice<=214000)])/len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们看到两种方法计算SalePrice的IQR的结果都是一样的，IQR占了50%的数据量。\n",
    "\n",
    "下面我计算地下室面积的IQR所占比例，同样我们用两种方法来计算:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1460.000000\n",
       "mean     1057.429452\n",
       "std       438.705324\n",
       "min         0.000000\n",
       "25%       795.750000\n",
       "50%       991.500000\n",
       "75%      1298.250000\n",
       "max      6110.000000\n",
       "Name: TotalBsmtSF, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.TotalBsmtSF.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "地下室面积(TotalBsmtSF)的IQR(Q3-Q1)所占比例: 0.5\n",
      "地下室面积(TotalBsmtSF)的IQR(Q3-Q1)所占比例: 0.5\n"
     ]
    }
   ],
   "source": [
    "print('地下室面积(TotalBsmtSF)的IQR(Q3-Q1)所占比例:',np.mean((df.TotalBsmtSF >= 795.75) & (df.TotalBsmtSF <= 1298.25)))\n",
    "\n",
    "print('地下室面积(TotalBsmtSF)的IQR(Q3-Q1)所占比例:',len(df[(df.TotalBsmtSF>=795.75)&(df.TotalBsmtSF<=1298.25)])/len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们想看一下销售价格IQR区间内的数据和地下室面积IQRQ区间内的数据是否有重合，我们将销售价格的IQR和地下室面积的IQR做一个并集，如果他们之间没有重合，那么并集所占比例正好为1，因为它正好覆盖整个数据集，如果他们之间有重合，那么并集所占比例将大于0.5且小于1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7143835616438357\n"
     ]
    }
   ],
   "source": [
    "sprice_q1= 129975\n",
    "sprice_q3=214000\n",
    "a = (df.SalePrice >= sprice_q1) & (df.SalePrice <= sprice_q3)\n",
    "\n",
    "tbs_q1=795.75\n",
    "tbs_q3 = 1298.25\n",
    "b = (df.TotalBsmtSF >= tbs_q1) & (df.TotalBsmtSF <= tbs_q3)\n",
    "\n",
    "print(np.mean(a | b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们计算房屋带空调和不带空调的IQR: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "没有空调的房屋销售价格IQR:  46500.0\n"
     ]
    }
   ],
   "source": [
    "q75, q25 = np.percentile(df.loc[df['CentralAir']=='N']['SalePrice'], [75,25])\n",
    "iqr = q75 - q25\n",
    "print('没有空调的房屋销售价格IQR: ', iqr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有空调的房屋销售价格IQR:  84410.0\n"
     ]
    }
   ],
   "source": [
    "q75, q25 = np.percentile(df.loc[df['CentralAir']=='Y']['SalePrice'], [75,25])\n",
    "iqr = q75 - q25\n",
    "print('有空调的房屋销售价格IQR: ', iqr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分层\n",
    "\n",
    "分层是一种从数据集中获取更多信息的方法，它将数据集划分为更小，更均匀的子集，通过分层我们可以创建新的特征。下面我们来创建一个新的特征:“房龄”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1460.000000\n",
       "mean       47.732192\n",
       "std        30.202904\n",
       "min         9.000000\n",
       "25%        19.000000\n",
       "50%        46.000000\n",
       "75%        65.000000\n",
       "max       147.000000\n",
       "Name: HouseAge, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HouseAge'] = 2019 - df['YearBuilt']\n",
    "df.HouseAge.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们将房龄划分成若干个区间,然后我们查看房龄的箱线图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"AgeGrp\"] = pd.cut(df.HouseAge, [9, 20, 40, 60, 80, 100, 147]) \n",
    "plt.figure(figsize=(12, 5)) \n",
    "sns.boxplot(x=\"AgeGrp\", y=\"SalePrice\", data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们看到房龄越小，价格中位数越高，这也是合理的因为新房子的价格应该要比老房子的价格高。房价似乎随着房龄的增加而最近减小，但是一个有趣的现象是房龄超过100岁的房屋销售价格似乎略微高于之前的（80,100）的房龄的房价。\n",
    "\n",
    "下面我们在房龄的基础上加入空调变量，我们想看看不同的房龄区间和房屋是否带空调对房价的影响："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "sns.boxplot(x=\"AgeGrp\", y=\"SalePrice\", hue=\"CentralAir\", data=df)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图中我们可以发现，小于40年房龄的房子都带有空调，大于40年房龄的房子开始出现不带空调的情况，这也应该是合理的，40年以上房龄的老房子，那个年底空调不知道发明了没有，即便已经发明了也应该是没有普及，所以不带空调的房子还是比较普遍。\n",
    "\n",
    "同样我们也可以在空调变量的基础上加上房龄变量，然后我查看它们与房价之间的关系："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "sns.boxplot(x=\"CentralAir\", y=\"SalePrice\", hue=\"AgeGrp\", data=df)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们发现在带有空调的房屋中，房价的变化趋势随着房龄的增加而减少，这个情况类似与之前单独展示房龄和房价的箱线图的情形一样，在不带空调的房屋中房龄都大于40年，100年以上房龄的房价似乎是最高的。\n",
    "\n",
    "接下来我们按照房龄和空调变量进行分组，然后统计住宅类型(BldgType)的数量分布情况:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BldgType               1Fam  2fmCon  Duplex  Twnhs  TwnhsE\n",
      "AgeGrp     CentralAir                                     \n",
      "(9, 20]    Y          0.782     NaN     NaN  0.046   0.172\n",
      "(20, 40]   Y          0.827     NaN   0.053  0.010   0.111\n",
      "(40, 60]   N          0.167   0.167   0.667    NaN     NaN\n",
      "           Y          0.813   0.008   0.067  0.059   0.053\n",
      "(60, 80]   N          0.556   0.148   0.296    NaN     NaN\n",
      "           Y          0.955   0.030   0.015    NaN     NaN\n",
      "(80, 100]  N          0.926   0.074     NaN    NaN     NaN\n",
      "           Y          0.992   0.008     NaN    NaN     NaN\n",
      "(100, 147] N          0.771   0.229     NaN    NaN     NaN\n",
      "           Y          0.860   0.120   0.020    NaN     NaN\n"
     ]
    }
   ],
   "source": [
    "df1 = df.groupby([\"AgeGrp\", \"CentralAir\"])[\"BldgType\"]\n",
    "df1 = df1.value_counts()\n",
    "df1 = df1.unstack()\n",
    "df1 = df1.apply(lambda x: x/x.sum(), axis=1) # 标准化处理(归一化)\n",
    "print(df1.to_string(float_format=\"%.3f\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们发现在所有的房屋中(不管房龄大小)住宅类型为\"1Fam\"的所占比重最大，房龄越大住宅类型越少， 80年以上的房龄大部分只有“1Fam”和“2fmCon”两种住宅类型。越古老的房子越趋向于没有空调。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 多变量分析\n",
    "\n",
    "前面我们所做的分析都是基于单个变量的分析，接下来我们要进行多个变量同时进行分析。\n",
    "\n",
    "## 散点图\n",
    "我们首先画一下居住面积(GrLivArea)和房屋销售价格的散点图，从散点图中我们发现房屋居住面积和销售价格呈现正相关趋势。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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         "mode": "markers",
         "name": "SalePrice",
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138000, 137450, 120000, 193000, 193879, 282922, 105000, 275000, 133000, 112000, 125500, 215000, 230000, 140000, 90000, 257000, 207000, 175900, 122500, 340000, 124000, 223000, 179900, 127500, 136500, 274970, 144000, 142000, 271000, 140000, 119000, 182900, 192140, 143750, 64500, 186500, 160000, 174000, 120500, 394617, 149700, 197000, 191000, 149300, 310000, 121000, 179600, 129000, 157900, 240000, 112000, 92000, 136000, 287090, 145000, 84500, 185000, 175000, 210000, 266500, 142125, 147500]}],\n",
       "                        {\"legend\": {\"bgcolor\": \"#F5F6F9\", \"font\": {\"color\": \"#4D5663\"}}, \"paper_bgcolor\": \"#F5F6F9\", \"plot_bgcolor\": \"#F5F6F9\", \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"\\u623f\\u5c4b\\u4ef7\\u683c\\u4e0e\\u5c45\\u4f4f\\u9762\\u79ef\\u6563\\u70b9\\u5206\\u5e03\"}, \"xaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"\\u5730\\u4e0a\\u5c45\\u4f4f\\u9762\\u79ef\"}, \"zerolinecolor\": \"#E1E5ED\"}, \"yaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"\\u9500\\u552e\\u4ef7\\u683c\"}, \"zerolinecolor\": \"#E1E5ED\"}},\n",
       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('0a4e0163-1b1c-4ebd-a287-51c6ade9685b');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iplot(\n",
    "    x='GrLivArea',\n",
    "    y='SalePrice',\n",
    "    xTitle='地上居住面积',\n",
    "    yTitle='销售价格',\n",
    "    mode='markers',\n",
    "    title='房屋价格与居住面积散点分布')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D密度联合图\n",
    "\n",
    "接下来我们要画2D密度联合图，我们要在在散点图的基础上画热力图，在顶部和右侧分布画对应热力图的居住面积和销售价格的密度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~tongzheming/152.embed\" height=\"550px\" width=\"600px\"></iframe>"
      ],
      "text/plain": [
       "<chart_studio.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace1 = go.Scatter(\n",
    "    x=df['GrLivArea'], y=df['SalePrice'], mode='markers', name='points',\n",
    "    marker=dict(color='rgb(102,0,0)', size=2, opacity=0.4)\n",
    ")\n",
    "trace2 = go.Histogram2dContour(\n",
    "    x=df['GrLivArea'], y=df['SalePrice'], name='密度', ncontours=20,\n",
    "    colorscale='Hot', reversescale=True, showscale=False\n",
    ")\n",
    "trace3 = go.Histogram(\n",
    "    x=df['GrLivArea'], name='地面居住面积密度',\n",
    "    marker=dict(color='rgb(102,0,0)'),\n",
    "    yaxis='y2'\n",
    ")\n",
    "trace4 = go.Histogram(\n",
    "    y=df['SalePrice'], name='价格密度', marker=dict(color='rgb(102,0,0)'),\n",
    "    xaxis='x2'\n",
    ")\n",
    "data = [trace1, trace2, trace3, trace4]\n",
    "\n",
    "layout = go.Layout(\n",
    "    showlegend=False,\n",
    "    autosize=False,\n",
    "    width=600,\n",
    "    height=550,\n",
    "    xaxis=dict(\n",
    "        domain=[0, 0.85],\n",
    "        showgrid=False,\n",
    "        zeroline=False\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        domain=[0, 0.85],\n",
    "        showgrid=False,\n",
    "        zeroline=False\n",
    "    ),\n",
    "    margin=dict(\n",
    "        t=50\n",
    "    ),\n",
    "    hovermode='closest',\n",
    "    bargap=0,\n",
    "    xaxis2=dict(\n",
    "        domain=[0.85, 1],\n",
    "        showgrid=False,\n",
    "        zeroline=False\n",
    "    ),\n",
    "    yaxis2=dict(\n",
    "        domain=[0.85, 1],\n",
    "        showgrid=False,\n",
    "        zeroline=False\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分类变量的分层探索 \n",
    "\n",
    "我们将继续探索居住面积(GrLivArea)和销售价格之间的关系,不过这次我们住宅类型(BldgType)分层的方式来研究，我们要按5种住宅类型，分别画出5个居住面积和销售价格的散点图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the format of your plot grid:\n",
      "[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]  [ (1,3) x3,y3 ]\n",
      "[ (2,1) x4,y4 ]  [ (2,2) x5,y5 ]  [ (2,3) x6,y6 ]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~tongzheming/154.embed\" height=\"400px\" width=\"800px\"></iframe>"
      ],
      "text/plain": [
       "<chart_studio.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace0 = go.Scatter(x=df.loc[df['BldgType'] == '1Fam']['GrLivArea'], y=df.loc[df['BldgType'] == '1Fam']['SalePrice'], mode='markers', name='1Fam')\n",
    "trace1 = go.Scatter(x=df.loc[df['BldgType'] == 'TwnhsE']['GrLivArea'], y=df.loc[df['BldgType'] == 'TwnhsE']['SalePrice'], mode='markers', name='TwnhsE')\n",
    "trace2 = go.Scatter(x=df.loc[df['BldgType'] == 'Duplex']['GrLivArea'], y=df.loc[df['BldgType'] == 'Duplex']['SalePrice'], mode='markers', name='Duplex')\n",
    "trace3 = go.Scatter(x=df.loc[df['BldgType'] == 'Twnhs']['GrLivArea'], y=df.loc[df['BldgType'] == 'Twnhs']['SalePrice'], mode='markers', name='Twnhs')\n",
    "trace4 = go.Scatter(x=df.loc[df['BldgType'] == '2fmCon']['GrLivArea'], y=df.loc[df['BldgType'] == '2fmCon']['SalePrice'], mode='markers', name='2fmCon')\n",
    "\n",
    "fig = tools.make_subplots(rows=2, cols=3)\n",
    "\n",
    "fig.append_trace(trace0, 1, 1)\n",
    "fig.append_trace(trace1, 1, 2)\n",
    "fig.append_trace(trace2, 1, 3)\n",
    "fig.append_trace(trace3, 2, 1)\n",
    "fig.append_trace(trace4, 2, 2)\n",
    "\n",
    "fig['layout'].update(height=400, width=800, title='按住宅类型分组查看居住面积与房屋销售价格散点')\n",
    "py.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在上图中我们发现，在5种住宅类型的散点图中，居住面积都与销售价格成正相关趋势。\n",
    "\n",
    "下面我们任然按5种住宅类型分层的方式来计算居住面积和销售价格的相关系数:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           GrLivArea  SalePrice\n",
      "GrLivArea   1.000000   0.738956\n",
      "SalePrice   0.738956   1.000000\n",
      "-----------------------------------\n",
      "           GrLivArea  SalePrice\n",
      "GrLivArea   1.000000   0.641622\n",
      "SalePrice   0.641622   1.000000\n",
      "-----------------------------------\n",
      "           GrLivArea  SalePrice\n",
      "GrLivArea   1.000000   0.490441\n",
      "SalePrice   0.490441   1.000000\n",
      "-----------------------------------\n",
      "           GrLivArea  SalePrice\n",
      "GrLivArea    1.00000    0.66924\n",
      "SalePrice    0.66924    1.00000\n",
      "-----------------------------------\n",
      "           GrLivArea  SalePrice\n",
      "GrLivArea   1.000000   0.498502\n",
      "SalePrice   0.498502   1.000000\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[df.BldgType==\"1Fam\", [\"GrLivArea\", \"SalePrice\"]].corr())\n",
    "print('-----------------------------------')\n",
    "print(df.loc[df.BldgType==\"TwnhsE\", [\"GrLivArea\", \"SalePrice\"]].corr())\n",
    "print('-----------------------------------')\n",
    "print(df.loc[df.BldgType=='Duplex', [\"GrLivArea\", \"SalePrice\"]].corr())\n",
    "print('-----------------------------------')\n",
    "print(df.loc[df.BldgType==\"Twnhs\", [\"GrLivArea\", \"SalePrice\"]].corr())\n",
    "print('-----------------------------------')\n",
    "print(df.loc[df.BldgType==\"2fmCon\", [\"GrLivArea\", \"SalePrice\"]].corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 两个分类变量的交叉分析 \n",
    "\n",
    "下面我们要对MSZoning(区域分类)和BldgType(住宅类型)这两个分类变量，进行交叉分析。首先我们统计它们的同现次数:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>BldgType</th>\n",
       "      <th>1Fam</th>\n",
       "      <th>2fmCon</th>\n",
       "      <th>Duplex</th>\n",
       "      <th>Twnhs</th>\n",
       "      <th>TwnhsE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C (all)</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FV</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RL</th>\n",
       "      <td>1025</td>\n",
       "      <td>16</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>139</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "BldgType  1Fam  2fmCon  Duplex  Twnhs  TwnhsE\n",
       "MSZoning                                     \n",
       "C (all)      9       1       0      0       0\n",
       "FV          38       0       0      9      18\n",
       "RH           9       2       3      0       2\n",
       "RL        1025      16      43     10      57\n",
       "RM         139      12       6     24      37"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.crosstab(df.MSZoning, df.BldgType)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 按列归一化\n",
    "\n",
    "我们按列来进行归一化，这样我们就可以看到MSZoning的值对应各个不同的BldgType值所占比重是多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>BldgType</th>\n",
       "      <th>1Fam</th>\n",
       "      <th>2fmCon</th>\n",
       "      <th>Duplex</th>\n",
       "      <th>Twnhs</th>\n",
       "      <th>TwnhsE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C (all)</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FV</th>\n",
       "      <td>0.584615</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.138462</td>\n",
       "      <td>0.276923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH</th>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RL</th>\n",
       "      <td>0.890530</td>\n",
       "      <td>0.013901</td>\n",
       "      <td>0.037359</td>\n",
       "      <td>0.008688</td>\n",
       "      <td>0.049522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>0.637615</td>\n",
       "      <td>0.055046</td>\n",
       "      <td>0.027523</td>\n",
       "      <td>0.110092</td>\n",
       "      <td>0.169725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "BldgType      1Fam    2fmCon    Duplex     Twnhs    TwnhsE\n",
       "MSZoning                                                  \n",
       "C (all)   0.900000  0.100000  0.000000  0.000000  0.000000\n",
       "FV        0.584615  0.000000  0.000000  0.138462  0.276923\n",
       "RH        0.562500  0.125000  0.187500  0.000000  0.125000\n",
       "RL        0.890530  0.013901  0.037359  0.008688  0.049522\n",
       "RM        0.637615  0.055046  0.027523  0.110092  0.169725"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.apply(lambda z: z/z.sum(), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 按行归一化\n",
    "\n",
    "我们按行来进行归一化，这样我们就可以看到BldgType的值对应各个不同的MSZoning值所占比重是多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>BldgType</th>\n",
       "      <th>1Fam</th>\n",
       "      <th>2fmCon</th>\n",
       "      <th>Duplex</th>\n",
       "      <th>Twnhs</th>\n",
       "      <th>TwnhsE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C (all)</th>\n",
       "      <td>0.007377</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FV</th>\n",
       "      <td>0.031148</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.209302</td>\n",
       "      <td>0.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH</th>\n",
       "      <td>0.007377</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.057692</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RL</th>\n",
       "      <td>0.840164</td>\n",
       "      <td>0.516129</td>\n",
       "      <td>0.826923</td>\n",
       "      <td>0.232558</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>0.113934</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.558140</td>\n",
       "      <td>0.324561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "BldgType      1Fam    2fmCon    Duplex     Twnhs    TwnhsE\n",
       "MSZoning                                                  \n",
       "C (all)   0.007377  0.032258  0.000000  0.000000  0.000000\n",
       "FV        0.031148  0.000000  0.000000  0.209302  0.157895\n",
       "RH        0.007377  0.064516  0.057692  0.000000  0.017544\n",
       "RL        0.840164  0.516129  0.826923  0.232558  0.500000\n",
       "RM        0.113934  0.387097  0.115385  0.558140  0.324561"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.apply(lambda z: z/z.sum(), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对三个分类变量分组统计 \n",
    "\n",
    "下面我要对\"CentralAir\", \"BldgType\", \"MSZoning\"这三个分类变量进行分组统计它们的同现次数，并进行归一化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>C (all)</th>\n",
       "      <th>FV</th>\n",
       "      <th>RH</th>\n",
       "      <th>RL</th>\n",
       "      <th>RM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CentralAir</th>\n",
       "      <th>BldgType</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">N</th>\n",
       "      <th>1Fam</th>\n",
       "      <td>0.073529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.426471</td>\n",
       "      <td>0.441176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2fmCon</th>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duplex</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Y</th>\n",
       "      <th>1Fam</th>\n",
       "      <td>0.003472</td>\n",
       "      <td>0.032986</td>\n",
       "      <td>0.004340</td>\n",
       "      <td>0.864583</td>\n",
       "      <td>0.094618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2fmCon</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duplex</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Twnhs</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.209302</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.232558</td>\n",
       "      <td>0.558140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TwnhsE</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.324561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "MSZoning              C (all)        FV        RH        RL        RM\n",
       "CentralAir BldgType                                                  \n",
       "N          1Fam      0.073529  0.000000  0.058824  0.426471  0.441176\n",
       "           2fmCon    0.066667  0.000000  0.000000  0.466667  0.466667\n",
       "           Duplex    0.000000  0.000000  0.166667  0.750000  0.083333\n",
       "Y          1Fam      0.003472  0.032986  0.004340  0.864583  0.094618\n",
       "           2fmCon    0.000000  0.000000  0.125000  0.562500  0.312500\n",
       "           Duplex    0.000000  0.000000  0.025000  0.850000  0.125000\n",
       "           Twnhs     0.000000  0.209302  0.000000  0.232558  0.558140\n",
       "           TwnhsE    0.000000  0.157895  0.017544  0.500000  0.324561"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"CentralAir\", \"BldgType\", \"MSZoning\"]).size().unstack().fillna(0).apply(lambda x: x/x.sum(), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们看到变量MSZoning的“RL”列所占比重是最大的，其中有空调的里面1Fam,RL所占比重最大为86.4%，没空调的里面,Duplex,RL所占比重最大为75%。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分类型和数值型混合变量分析\n",
    "\n",
    "下面我们要画一个小提琴图，用以显示在不同的住宅类型的情况下销售价格的分布情况:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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    "trace0 = go.Box(\n",
    "    y=df.loc[df['BldgType'] == '1Fam']['SalePrice'],\n",
    "    name = '1Fam',\n",
    "    marker = dict(\n",
    "        color = 'rgb(214, 12, 140)',\n",
    "    )\n",
    ")\n",
    "trace1 = go.Box(\n",
    "    y=df.loc[df['BldgType'] == 'TwnhsE']['SalePrice'],\n",
    "    name = 'TwnhsE',\n",
    "    marker = dict(\n",
    "        color = 'rgb(0, 128, 128)',\n",
    "    )\n",
    ")\n",
    "trace2 = go.Box(\n",
    "    y=df.loc[df['BldgType'] == 'Duplex']['SalePrice'],\n",
    "    name = 'Duplex',\n",
    "    marker = dict(\n",
    "        color = 'rgb(12, 102, 14)',\n",
    "    )\n",
    ")\n",
    "trace3 = go.Box(\n",
    "    y=df.loc[df['BldgType'] == 'Twnhs']['SalePrice'],\n",
    "    name = 'Twnhs',\n",
    "    marker = dict(\n",
    "        color = 'rgb(10, 0, 100)',\n",
    "    )\n",
    ")\n",
    "trace4 = go.Box(\n",
    "    y=df.loc[df['BldgType'] == '2fmCon']['SalePrice'],\n",
    "    name = '2fmCon',\n",
    "    marker = dict(\n",
    "        color = 'rgb(100, 0, 10)',\n",
    "    )\n",
    ")\n",
    "data = [trace0, trace1, trace2, trace3, trace4]\n",
    "layout = go.Layout(\n",
    "    title = \"不同住宅类型的销售价格散点图\"\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data,layout=layout)\n",
    "iplot(fig)"
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     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~tongzheming/156.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<chart_studio.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "for i in range(0,len(pd.unique(df['BldgType']))):\n",
    "    trace = {\n",
    "            \"type\": 'violin',\n",
    "            \"x\": df['BldgType'][df['BldgType'] == pd.unique(df['BldgType'])[i]],\n",
    "            \"y\": df['SalePrice'][df['BldgType'] == pd.unique(df['BldgType'])[i]],\n",
    "            \"name\": pd.unique(df['BldgType'])[i],\n",
    "            \"box\": {\n",
    "                \"visible\": True\n",
    "            },\n",
    "            \"meanline\": {\n",
    "                \"visible\": True\n",
    "            }\n",
    "        }\n",
    "    data.append(trace)\n",
    "\n",
    "        \n",
    "fig = {\n",
    "    \"data\": data,\n",
    "    \"layout\" : {\n",
    "        \"title\": \"\",\n",
    "        \"yaxis\": {\n",
    "            \"zeroline\": False,\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "\n",
    "py.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以看到1Fam住宅类型的SalesPrice分布略微右偏，而对于其他住宅类型，SalePrice分布接近正太分布。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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